semvec/pypi-packages huggingface dataset:
onepot 0.1.2
Python client for the onepot API
Requires Python: >=3.11

# onepot-python

Python client for the onepot API.

## Installation

```bash
uv add onepot
# or
pip install onepot
```

## Usage

```python
from onepot import Client

client = Client(api_key="your-api-key")

response = client.search(
    smiles_list=["Cc1c(C(=O)Nc2ccc(N3CC(C)OC(C)C3)nc2)cccc1-c1ccc(OC(F)(F)F)cc1", ...],
    max_results=10,
    include_chemistry_risk_score=True,
)
```

## Pricing

Credits are charged per SMILES in the query:

| Option | Credits per SMILES |
|--------|-------------------|
| Base search | 1 |
| `include_chemistry_risk=True` | 5 |
| `include_chemistry_risk_score=True` | 10 |

## Response Format

```python
{
    "queries": [
        {
            "query_smiles": "Cc1c(C(=O)Nc2ccc(N3CC(C)OC(C)C3)nc2)cccc1-c1ccc(OC(F)(F)F)cc1",
            "query_inchikey": "VZZJRYRQSPEMTK-CALCHBBNSA-N",
            "results": [
                {
                    "chemistry_risk": "medium",     # if include_chemistry_risk=True
                    "chemistry_risk_score": 0.5,    # if include_chemistry_risk_score=True
                    "inchikey": "VZZJRYRQSPEMTK-UHFFFAOYSA-N",
                    "price_usd": 590,
                    "similarity": 1.0,
                    "smiles": "Cc1c(C(=O)Nc2ccc(N3CC(C)OC(C)C3)nc2)cccc1-c1ccc(OC(F)(F)F)cc1",
                    "supplier_risk": "high",
                },
                ...
            ]
        },
        ...
    ],
    "credits_used": 10,
    "credits_remaining": 990
}
```
onepot-client 0.1.2
Python client for the onepot API
Requires Python: >=3.11

# onepot-client

Python client for the onepot API.

## Installation

```bash
uv add onepot
# or
pip install onepot
```

## Usage

```python
from onepot import Client

client = Client(api_key="your-api-key")

response = client.search(
    smiles_list=["Cc1c(C(=O)Nc2ccc(N3CC(C)OC(C)C3)nc2)cccc1-c1ccc(OC(F)(F)F)cc1", ...],
    max_results=10,
    include_chemistry_risk_score=True,
)
```

## Pricing

Credits are charged per SMILES in the query:

| Option | Credits per SMILES |
|--------|-------------------|
| Base search | 1 |
| `include_chemistry_risk=True` | 5 |
| `include_chemistry_risk_score=True` | 10 |

## Response Format

```python
{
    "queries": [
        {
            "query_smiles": "Cc1c(C(=O)Nc2ccc(N3CC(C)OC(C)C3)nc2)cccc1-c1ccc(OC(F)(F)F)cc1",
            "query_inchikey": "VZZJRYRQSPEMTK-CALCHBBNSA-N",
            "results": [
                {
                    "chemistry_risk": "medium",     # if include_chemistry_risk=True
                    "chemistry_risk_score": 0.5,    # if include_chemistry_risk_score=True
                    "inchikey": "VZZJRYRQSPEMTK-UHFFFAOYSA-N",
                    "price_usd": 590,
                    "similarity": 1.0,
                    "smiles": "Cc1c(C(=O)Nc2ccc(N3CC(C)OC(C)C3)nc2)cccc1-c1ccc(OC(F)(F)F)cc1",
                    "supplier_risk": "high",
                },
                ...
            ]
        },
        ...
    ],
    "credits_used": 10,
    "credits_remaining": 990
}
```
numba-cuda 0.27.0
CUDA target for Numba
Requires Python: >=3.9

<div align="center"><img src="docs/source/_static/numba-green-icon-rgb.svg" width="200"/></div>

# Numba CUDA Target

The CUDA target for Numba. Please visit the [official
documentation](https://nvidia.github.io/numba-cuda) to get started!


To report issues or file feature requests, please use the [issue
tracker](https://github.com/NVIDIA/numba-cuda/issues).

To raise questions or initiate discussions, please use the [Numba Discourse
forum](https://numba.discourse.group).

## Installation with pip or conda

Please refer to the [Installation documentation](https://nvidia.github.io/numba-cuda/user/installation.html#installation-with-a-python-package-manager).


## Installation from source

Install as an editable install:

```
pip install -e .
```

If you want to manage all run-time dependencies yourself, also pass the `--no-deps` flag.

## Running tests

Tests must be run from the `testing` folder, which contains the pytest
configuration and code to generate binaries used during the tests. The test
binaries need to be built on the system on which the tests are run, so that
they are compiled for the appropriate compute capability.

```
cd testing
# Optionally, build test binaries and point to their location for the test suite
make -j $(nproc)
export NUMBA_CUDA_TEST_BIN_DIR=`pwd`
# Execute tests
pytest -n auto -v --dist loadscope
```

Alternatively, you can use [pixi](https://pixi.sh/latest/installation/) to wrap all of that up for you:

```
# run tests against CUDA 13
pixi run -e cu13 test -n auto -v --dist loadscope
```


Testing should discover the `numba.cuda` module from the `numba_cuda` package. You
can check where `numba.cuda` files are being located by running

```
python -c "from numba import cuda; print(cuda.__file__)"
```

which will show a path like:

```
<path to numba-cuda repo>/numba_cuda/numba/cuda/__init__.py
```

## Contributing Guide

Review the
[CONTRIBUTING.md](https://github.com/NVIDIA/numba-cuda/blob/main/CONTRIBUTING.md)
file for information on how to contribute code and issues to the project.
gpstime 0.10.0
GPS-aware datetime module
Requires Python: >=3.6

GPS-aware datetime module
=========================

This package provides GPS time conversion utilities, including a
gpstime subclass of the built-in datetime class with the addition of
GPS time parsing and conversion methods.

Leap second data, necessary for GPS time conversion, is expected to be
provided by the core libc Time Zone Database tzdata (on linux).  If
for some reason the tzdata leapsecond file is not available, a local
cache of the IERS leap second record will be maintained:

  https://hpiers.obspm.fr/iers/bul/bulc/ntp/leap-seconds.list

The package can be executed as a command-line GPS conversion utility.
In this manor it is a rough work-alike to the LIGO "tconvert" utility.