semvec/pypi-packages huggingface dataset:
goharvest-simforge 0.1.0
Simforge client for provider-based API calls
Requires Python: <3.13,>=3.11

# Kirby

Kirby client for provider-based API calls.

## Installation

For local development:

```bash
cd kirby
poetry install
```

Or install as an editable package from the parent directory:

```bash
poetry add --editable ../kirby
```

## Usage

```python
from kirby import Kirby

client = Kirby(
    provider="openAI",
    key="your-api-key",
    model="gpt-4",
    kirby_url="https://your-kirby-instance.com",  # Optional, can use KIRBY_URL env var
    kirby_user_id="user-id",  # Optional, can use KIRBY_USER_ID env var
)

result = client.call("method_name", arg1="value1", arg2="value2")
```

## Configuration

The `kirby_url` and `kirby_user_id` can be provided either:
- As constructor arguments
- As environment variables (`KIRBY_URL` and `KIRBY_USER_ID`)
itac-pylib 0.1.2
iTAC API client library
Requires Python: >=3.12

# iTAC REST API lib
[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg)](https://www.python.org/)
[![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://lbesson.mit-license.org/)
[![Ask Me Anything !](https://img.shields.io/badge/Ask%20me-anything-1abc9c.svg)](https://github.com/ANUVAMAON)

RESTful API for iTAC.

*Use iTAC **OnlineHelp** for IMSAPi-Description.*

# How to use
**Fill station or user parameters**

**Station parameters**
```python
STATION_NUMBER = " "
STATION_PASSWORD = " "
```

**User parameters**
```python
USER = " "
PASSWORD = " "
```
**Client**
```python
CLIENT = " "
```
**Registration type**
```python
REG_TYPE = "S"
# S for station
# U for user
```

**System identification**
```python
SYS_ID = "TestApp"
```
# Example
```python
from itac_pylib.client import Client

BASE_URL = "https://api-url/mes/imsapi/rest/actions/"
STATION_NUMBER = "StationNumber"
STATION_PASSWORD = ""
USER = ""
PASSWORD = ""
CLIENT = "01"
REG_TYPE = "S"
SYS_ID = "TestApp"

itac = Client(
    base_url=BASE_URL,
    station_number=STATION_NUMBER,
    station_password=STATION_PASSWORD,
    client=CLIENT,
    reg_type=REG_TYPE,
    sys_id=SYS_ID,
)

test = itac.attribAppendAttributeValues(
    stationNumber="StationNumber",
    objectType=0,
    objectNumber="Test-0001",
    objectDetail="-1",
    bookDate="-1",
    allowOverWrite=0,
    attributeUploadValues=["", "", 0],
)
print("Return value: ", test.return_value)
print("attributeResultValues: ", test.attributeResultValues)
print("ERROR_CODE: ", test.attributeResultValues["ERROR_CODE"])
```

### Output
```bash
Return value:  5
attributeResultValues:  {'ATTRIBUTE_CODE': '', 'ATTRIBUTE_VALUE': '', 'ERROR_CODE': '-904'}
ERROR_CODE:  -904
```
natstat-cli 0.1.0
A command line interface for the National Statistical API
Requires Python: >=3.14

# natstat-cli

A command line interface for the National Statistical API

## Installation

The CLI can be installed with pip.

```bash
git clone https://github.com/jhelderman/natstat-cli.git
cd natstat-cli
pip install .
```

## Configuration

This CLI requires an API key for National Statistical. This can be acquired
from the [NatStat website](natstat.com). The API key is configured with an
environment variable.

```bash
export NATSTAT_API_KEY=change-me
```

## Usage

The CLI has a subcommand for each endpoint. These can be seen in the help
message for the base command.

```
❯ natstat --help
Usage: natstat [OPTIONS] COMMAND [ARGS]...

  Query the NatStat API

Options:
  --help  Show this message and exit.

Commands:
  games        Game data
  leaguecodes  League codes
  moneyline    Moneyline records
  overunder    Over/Under records
  playbyplay   Play-by-play data
  playerperfs  Player performance data
  players      Player data
  pointspread  Point spread records
  seasons      Season info
  teamcodes    Team codes
  teamperfs    Team performance data
  teams        Team data
  venues       Venue data
```

Each subcommand has a help message that shows the available options.

```bash
natstat games --help
```

The following command queries for the NBA games that were played on
2025-11-23. Results are output to the standard output in CSV format.

```bash
natstat games --date 2025-11-23 --service nba
```

NatStat responses are paginated to limit the number of records per
response. This CLI automatically requests all the pages for a given
request. For example, the following command will fetch the NBA game
data for the 2022 season.

```bash
natstat games --season 2022 --service nba
```

To disable this behavior and only fetch one page, pass the `--one-page`
flag.

```bash
natstat games --season 2022 --service nba --one-page
```
spark-dsg 1.1.3
Library for representing 3D scene graphs
Requires Python: >=3.8

[![Spark-DSG Build and Test](https://github.com/MIT-SPARK/Spark-DSG/actions/workflows/ci.yaml/badge.svg)](https://github.com/MIT-SPARK/Spark-DSG/actions/workflows/ci.yaml)

## Spark-DSG

This is the core c++ library that contains the dynamic scene graph data-structure used by Hydra. It also has python bindings.

### Change Notes

- 4/22/24: Python bindings and unit tests are built by default. You will need to install relevant dependencies (via `sudo apt install python3-dev` or `rosdep`) or disable the bindings and tests via cmake options.
- 3/16/24: Updated dependencies to use system libraries for `nlohmann_json`. You will need to install it either via `sudo apt install nlohmann-json3-dev` or use `rosdep` to update dependencies.

### Acknowledgements and Disclaimer

**Acknowledgements:** This work was partially funded by the AIA CRA FA8750-19-2-1000, ARL DCIST CRA W911NF-17-2-0181, and ONR RAIDER N00014-18-1-2828.

**Disclaimer:** Research was sponsored by the United States Air Force Research Laboratory and the United States Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States Air Force or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

### Building for Python

  1. Install requirements and make a virtual environment:

```bash
sudo apt install python3-venv libzmqpp-dev nlohmann-json3-dev
mkdir /path/to/environment
cd /path/to/environment
python3 -m venv dsg  # or some other environment name

# you may also want to upgrade pip, though it shouldn't be necessary
# source dsg/bin/activate
# pip install --upgrade pip
```

  2. Install the python package
```bash
source /path/to/dsg/environment/bin/activate
git clone git@github.com:MIT-SPARK/Spark-DSG.git
pip install ./Spark-DSG
```

### Python Bindings Usage

See [this notebook](examples/python_api.py) for some examples for the bindings (you'll want to clone the repo, even if you installed from github).
You'll want to install `jupyter` and `jupytext` if you want to run it as a notebook, though you can also just run it directly as a python script.
You can find an example scene graph [here](https://drive.google.com/file/d/1jwcjrE4-6PvOgEgJipETkQaLgC43biFT/view?usp=sharing).

### Python API documentation

Generating the python documentation should be as simple as:

```
source /path/to/dsg/environment/bin/activate
cd doc
pip install sphinx  # if you haven't already
make html
python -m http.server  # to serve them locally
```

### Building For ROS

This repository is a valid ROS package and should build if placed in a workspace.