semvec/pypi-packages huggingface dataset:
tair-pulse 1.3.4
TairPulse is a tool to visualize the latency and availability of Tair/Redis instances.
Requires Python: >=3.6

# tair-pulse

tair-pulse is a tool for visualizing the latency and availability of Tair/Redis instances.

![image.png](https://s2.loli.net/2025/03/06/fdOBVr9DGRWKycb.png)

## Installation

tair-pulse requires `Python 3.8` or later.

Install it using `pip` or your preferred PyPI package manager.

```bash
pip install tair-pulse
```

## Usage

```bash
usage: tair-pulse [-h] [--host HOST] [--port PORT] [--password PASSWORD]
                  [--cluster] [--max-rt MAX_RT]
```

### Optional Arguments:

- `-h, --help`: Show the help message and exit.
- `--host HOST`: Specify the server hostname (default is 127.0.0.1).
- `--port PORT`: Specify the server port (default is 6379).
- `--password PASSWORD`: Specify the password for Tair/Redis authentication.
- `--cluster`: Specify if the server is a node of the Tair/Redis cluster.
- `--max-rt MAX_RT`: Print the key that exceeds the specified maximum response time (default is 0).
- `--fork`: Use `save` to trigger fork(2) in order to test latency.

### Examples:

Run tair-pulse with the default configuration against 127.0.0.1:6379:

```bash
$ tair-pulse
```

Test an Aliyun Tair instance without a password:

```bash
$ tair-pulse --host r-bp1qf8wio5zkp01pzt.redis.rds.aliyuncs.com
```

Test a cluster instance with a password, where one of the nodes is 192.168.10.1:7000:

```bash
$ tair-pulse --host 192.168.10.1 --port 7000 --password 123456 --cluster
```

Note: Replace the values in the examples with your actual server hostname, port, and password if applicable.
synrecon 0.0.1
Utility for reaction modeling using graph grammar
Requires Python: >=3.11

# SynRecon

Reconstruct molecular formulas from exact (monoisotopic) mass.

Given a neutral **exact (monoisotopic) mass**, **SynRecon** searches for a **non-negative integer composition** over a chosen element set that matches the mass within a configurable tolerance (default `1e-5` Da). Enumeration is performed using integer scaling with a fast DFS enumeration and an optional ILP backend. Scope: **basic single best formula**; optional chemistry filters (DBE ≥ 0, even-electron rule, nitrogen rule) can be enabled to tighten results.

**Highlights**
- Integer-weight DFS enumeration for raw candidate formulas.
- Optional chemical filters: DBE, even-electron rule, nitrogen rule.
- Optional simple isotopologue expansion (e.g. D for H).
- Small, dependency-light core; optional ILP backend for constrained searches.

---

## Install

```bash
pip install synrecon
```
## Quick examples

### Python API (recommended)

```python
from synrecon.solver import solve_formula, enumerate_formulas

# single best solution (or None)
sol = solve_formula(
    180.063388104,
    elements=["C", "H", "O"],
    abs_tol_da=1e-5,
    enforce_dbe=True,
)

if sol is None:
    print("No solution found within tolerance.")
else:
    print("Best formula:", sol.formula)
    print("Calculated mass:", sol.mass_calc)
    print("Absolute error (Da):", sol.abs_error)
    print("DBE:", sol.dbe)

# enumerate several candidates (more verbose)
candidates = enumerate_formulas(
    180.063388104,
    elements=["C", "H", "O"],
    abs_tol_da=1e-5,
    enforce_dbe=True,
    max_candidates=20,
    sort_by="abs_error",
)

print("\nTop candidates:")
for s in candidates[:10]:
    print(f"{s.formula:12s} mass={s.mass_calc:.6f} err={s.abs_error:.6e} dbe={s.dbe}")
```
### CLI

```bash
synrecon solve --mass 180.063388104 \
              --elements "C H O" \
              --tol 1e-5 \
              --dbe \
              --allow-isotopes \
              --labels "D T"
```


## Contributing
- [Tieu-Long Phan](https://tieulongphan.github.io/)

## Publication

[**SynRecon**:]()


## License

This project is licensed under MIT License - see the [License](LICENSE) file for details.

## Acknowledgments

This project has received funding from the European Unions Horizon Europe Doctoral Network programme under the Marie-Skłodowska-Curie grant agreement No 101072930 ([TACsy](https://tacsy.eu/) -- Training Alliance for Computational)