semvec/pypi-packages huggingface dataset:
lnctApi 0.0.6
LNCT API wrapper, written in Python.
License: MIT
Requires Python: >=3.10

# LNCT API
LNCT API wrapper, written in Python.

## Installation
Install using Pypi:
```bash
pip install lnctApi
```

## Usage
Example code to get Main Result:
```python
import lnctApi

student = lnctApi.accsoft(USER_ID, PASSWORD)
print(student.profile())
```

## Contact 
Chirag
<br>[cro@chirag.software](mailto:cro@chirag.software)
vagrant-init-generator 0.0.5
A tool to generate Vagrant init files with custom configurations
License: MIT

# Vagrant Init Generator

## Overview

`vagrant-init-generator` is a command-line tool that simplifies the process of generating Vagrant `Vagrantfile` with custom configurations. It allows users to specify CPU, RAM, machine name, host name, IP address, forwarded ports, and provisioning commands easily through user-friendly prompts or default values.

## Features

- Generate `Vagrantfile` with custom configurations
- Interactive prompts for user input (CPU, RAM, machine name, host name, IP address, forwarded ports, provision commands)
- Support for specifying forwarded ports and provisioning commands
- Default values for skipped prompts
- Easy installation and usage

## Installation

To install `vagrant-init-generator`, you can use `pip`:

```bash
pip install vagrant-init-generator
```
renewenergy 0.1.2
A great package to run aanalysis to help predict the renewable energy output of a country.
License: MIT
Requires Python: <4.0,>=3.9

# renewenergy

A great package to run aanalysis to help predict the renewable energy output of a country.

[![codecov](https://codecov.io/gh/DSCI-310-2024/renewenergy/graph/badge.svg?token=X00ruRbQWc)](https://codecov.io/gh/DSCI-310-2024/renewenergy) ![PyPI](https://img.shields.io/pypi/v/renewenergy?label=pypi%20package) [![Documentation Status](https://readthedocs.org/projects/renewenergy/badge/?version=latest)](https://renewenergy.readthedocs.io/en/latest/?badge=latest)
      



## Installation

```bash
$ pip install renewenergy
```

## Usage
```
from renewenergy.clean_data import clean_data
from renewenergy.create_scatter_plots import create_scatter_plots
from renewenergy.reading_data import reading_data
from renewenergy.impute_split import impute_split
from renewenergy.split_xy_columns import split_xy_columns
from renewenergy.plot_rmse import plot_rmse

import pandas as pd
from matplotlib import pyplot as plt
```

## Contributing

Interested in contributing? Check out the [contribution guidelines](https://github.com/DSCI-310-2024/renewenergy/blob/main/CONTRIBUTING.md). Please note that this project is released with a [Code of Conduct](https://github.com/DSCI-310-2024/renewenergy/blob/main/CONDUCT.md). By contributing to this project, you agree to abide by its terms.

## License

`renewenergy` was created by Neha Menon, Caden Chan, Peter Chen, Tak Sripratak. It is licensed under the terms of the MIT license.

## Credits

`renewenergy` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
vagrant-file-generator 0.0.4
A tool to generate Vagrant init files with custom configurations
License: MIT

# Vagrant Init Generator

## Overview

`vagrant-init-generator` is a command-line tool that simplifies the process of generating Vagrant `Vagrantfile` with custom configurations. It allows users to specify CPU, RAM, machine name, host name, IP address, forwarded ports, and provisioning commands easily through user-friendly prompts or default values.

## Features

- Generate `Vagrantfile` with custom configurations
- Interactive prompts for user input (CPU, RAM, machine name, host name, IP address, forwarded ports, provision commands)
- Support for specifying forwarded ports and provisioning commands
- Default values for skipped prompts
- Easy installation and usage

## Installation

To install `vagrant-init-generator`, you can use `pip`:

```bash
pip install vagrant-init-generator
```
vagrant-file-generator-v0.3 0.0.3
A tool to generate Vagrant init files with custom configurations
License: MIT

# Vagrant Init Generator

## Overview

`vagrant-init-generator` is a command-line tool that simplifies the process of generating Vagrant `Vagrantfile` with custom configurations. It allows users to specify CPU, RAM, machine name, host name, IP address, forwarded ports, and provisioning commands easily through user-friendly prompts or default values.

## Features

- Generate `Vagrantfile` with custom configurations
- Interactive prompts for user input (CPU, RAM, machine name, host name, IP address, forwarded ports, provision commands)
- Support for specifying forwarded ports and provisioning commands
- Default values for skipped prompts
- Easy installation and usage

## Installation

To install `vagrant-init-generator`, you can use `pip`:

```bash
pip install vagrant-init-generator
```
vagrant-file-generator-v0.2 0.0.2
A tool to generate Vagrant init files with custom configurations
License: MIT

# Vagrant Init Generator

## Overview

`vagrant-init-generator` is a command-line tool that simplifies the process of generating Vagrant `Vagrantfile` with custom configurations. It allows users to specify CPU, RAM, machine name, host name, IP address, forwarded ports, and provisioning commands easily through user-friendly prompts or default values.

## Features

- Generate `Vagrantfile` with custom configurations
- Interactive prompts for user input (CPU, RAM, machine name, host name, IP address, forwarded ports, provision commands)
- Support for specifying forwarded ports and provisioning commands
- Default values for skipped prompts
- Easy installation and usage

## Installation

To install `vagrant-init-generator`, you can use `pip`:

```bash
pip install vagrant-init-generator
```
pynyairbnb 0.4.0
A package for DSCI 310 Group's Airbnb Analysis.
License: MIT
Requires Python: >=3.9

# pynyairbnb

A package for DSCI 310 Group's Airbnb Analysis.

[![codecov](https://codecov.io/gh/DSCI-310-2024/pynyairbnb/graph/badge.svg?token=duRYHnZQ12)](https://codecov.io/gh/DSCI-310-2024/pynyairbnb)

## Installation

```bash
$ pip install pynyairbnb
```

## Usage

`pynyairbnb` can be used to load data from insideairbnb.com's NYC Airbnb Open Data, perform all necessary preprocessing necessary, generate visualizations, build a knn-classification model and conduct a hyperparameter optimization on the model as follows:

```python
from pynyairbnb.data_preprocessing import data_preprocessing
from pynyairbnb.plotting import plot_pynyairbnb
from pynyairbnb.pynyairbnb import nyairbnb_analysis

data_preprocessing("example-link-data.csv", "documents/data_files")  # url to your data and path to save your data
plot_pynyairbnb("documents/data_files/train_df.csv", "documents/data_figures", "documents/data_tables") # path to data files and output paths to save figures and tables
nyairbnb_analysis("documents/data_files", "documents/data_tables") # path to data files and output path to save tables
```

All outputs get saved as .csv or .jpg files that you can read in using the `pandas.read_csv()` function or the `matplotlib.pyplot.imread()` function respectively.

## Contributing

Interested in contributing? Check out the [contributing guidelines](./CONTRIBUTING.md). Please note that this project is released with a [Code of Conduct](./CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.

## License

`pynyairbnb` was created by by the members of Group 9 for DSCI 310. It is licensed under the terms of the [MIT license](./LICENSE).

_Please refer to `LICENSE.md` for detailed licensing information._

## Credits

`pynyairbnb` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).

This package also utilizes New York City Airbnb Open Data from [insideairbnb.com](http://insideairbnb.com/get-the-data/) for example demonstrations.

## Contributors

Rashi Selarka

Riddhi Battu

Oliver Gullery

Prithvi Sureka
lineartest 0.4.4
A testing framework working with Starlette TestClient.
License: MIT
Requires Python: <4.0,>=3.10

# LinearTest
A testing framework working with Starlette TestClient.

- **Source code**: https://github.com/fanyf22/lineartest/
- **Documentation**: https://fanyf22.github.io/lineartest/

## Dependencies

```toml
python = "^3.10"
pydantic = "^2.6.4"
pyyaml = "^6.0.1"
httpx = "^0.27.0"
```

## Installing

The package is published on https://pypi.org/lineartest.

Use `pip` or similar commands to install it:

```shell
pip install lineartest
```

## Features

- `lineartest.schedule` helps you run tests one-by-one in order, and the tests run later can retrieve the results from the tests run earlier.
- `lineartest.client` helps you log the detail of a request and the response easily.

## Example

```python
from pydantic import BaseModel
from starlette.testclient import TestClient
from lineartest import LinearClient, schedule

# import your ASGI app here for testing
...

test_client = TestClient(app)
client = LinearClient(test_client)


# add `test_login` function to schedule
@schedule
def test_login(username, password):
    class Response(BaseModel):
        success: bool
        token: str

    # the parameters are the same as `TestClient`
    res = client.post('/login', data={
        'username': username,
        'password': password
    }, model=Response)  # use `model` parameter to specify a response model

    assert res.success
    return res.token    # the result will be stored by `schedule`

# add `test_resource` function to schedule
@schedule
def test_resource():
    # use `schedule.get` to retrieve history result
    token = schedule.get(test_login)

    class Response(BaseModel):
        success: bool

    res = client.get('/', headers={
        'Authorization': f'Bearer {token}'
    }, model=Response)

    assert res.success


# run the schedule
# you may pass positional or keyword arguments
schedule.run('username', password='password')
```

## License

This project is licensed under the terms of the MIT License.
python-espresso 1.8.4
A tiny type-safe expression parser in Python
License: MIT

# Espresso â˜•

A tiny, type-safe expression parser in python.

### This documentation is a WIP

## Rationale

In certain cases, there is need for a safe way for the back-end to call functions provided by the client, espresso addresses this through a type-safety post-processing setup to ensure the function always receives the type it was defined with.

## Example

```python
import espresso

import random
import string

# An interface between the interpreter frontend and backend
ctx = espresso.Context()

# Namespaces are automatically resolved. In this case, within the space
# random, integer would be defined. Functionally this is syntactic sugar,
# the namespaces are simply nested dictionaries.
ctx.define_function(
    random.randint, ["random", "integer"], ["int", "int"]
)

# Variadic typing is also supported. The method will be passed an array of
# all the arguments (of a singular type).
ctx.define_function(
    random.choice, ["random", "oneOf"], ["*any"]
)
ctx.define_function(
    random.choice, ["random", "oneOfInteger"], ["*int"]
)
ctx.define_function(
    random.choice, ["random", "oneOfString"], ["*str"]
)

def random_string(length=32):
    return "".join(
        random.choice(string.ascii_letters + string.digits) for i in range(length)
    )


# Optional typing, will pass None if not provided
ctx.define_function(
    random_string, ["random", "string"], ["str?"]
)

ctx.eval("random.integer(0, 10)")
```

## Integration and Usage

To see how espresso is integrated into a production SaaS, you can see the [YAML Resolver](https://github.com/aadv1k/project-bombay/tree/master/core/YAMLResolver.py) which defines a bunch of functions and handles errors.

```yaml
- id: random.string(32)
- first_name: person.firstName
- last_name: person.lastName
- age: random.integer(0, 32)
- plan: choices.oneOf("beta", "pro", "free")
```