semvec/pypi-packages huggingface dataset:
dataorigin 1.6.4
DataOrigin utilities in a single library.
Requires Python: >=3.11

# DataOrigin Package

This package provides core utilities for the DataOrigin project, focusing on leveraging AI-driven data insights for sales prospecting and lead generation in the B2B event space.

## Installation

To install the `dataorigin` package, you can use pip:

```bash
pip install dataorigin
```

## Usage

Example usage for `google_sheets.py` (assuming you have configured credentials using environment variables):

- `GOOGLE_SERVICE_ACCOUNT_JSON`: full Service Account JSON as a string (recommended for containers)
- `GOOGLE_APPLICATION_CREDENTIALS`: absolute path to Service Account JSON
- `GOOGLE_OAUTH_CLIENT_SECRET_FILE`: path to OAuth client secret file (interactive, local dev)
- `GOOGLE_OAUTH_TOKEN_FILE`: where to persist OAuth token (default: `token.json`)
- `GOOGLE_OAUTH_PORT`: OAuth local server port (default: `0`)

```python
import os
import pandas as pd
from dataorigin.google_sheets import upsert_google_sheet, read_google_sheet

# Example: Write data to a spreadsheet
df = pd.DataFrame([{"a": 1, "b": 2}, {"a": 3, "b": 4}])

res = upsert_google_sheet(
    df=df,
    spreadsheet_id="YOUR_SPREADSHEET_ID",           # or use spreadsheet_title="..."
    sheet_name="data",                              # optional: target sheet (created if missing)
    folder_id=os.getenv("GDRIVE_FOLDER_ID"),         # optional
    clear=True,
    value_input_option="USER_ENTERED",              # "RAW" or "USER_ENTERED"
    rename_sheet=False
)

# Example: Read data from a spreadsheet (reads the first sheet by default)
data = read_google_sheet(spreadsheet_id="YOUR_SPREADSHEET_ID")
print(data)
```

## License

This project is licensed under the GNU General Public License v3.0 - see the `LICENSE` file for details.
toycrypto 0.6.1
Modules that can be used to illustrate and explore cryptographic concepts. This should never be used for security purposes.
Requires Python: >=3.12

# Toy cryptographic utilities

[![PyPI](https://img.shields.io/pypi/v/toycrypto?label=pypi%20package)][published]
[![Documentation][doc-build-badge]][documentation]
[![mypy status][type-badge]](https://mypy.readthedocs.io/en/stable/)
[![ruff status][lint-badge]](https://docs.astral.sh/ruff/)
[![pytest status][test-badge]](https://docs.pytest.org/en/stable/)
![Doctest status][doctest-badge]
[![CodeFactor][codefactor-badge]](https://www.codefactor.io/repository/github/jpgoldberg/toy-crypto-math)
[![License: MIT](https://img.shields.io/badge/license-MIT-C06524)](https://github.com/jpgoldberg/toy-crypto-math/blob/main/LICENSE.txt)

----

This is a collection of utilities that can be used for illustrating or
exploring some cryptographic concepts.
Although it includes implementations of some cryptographic algorithms,
these are **not secure** implementations.

See the [documentation] for use (or not) and [reasons why this exists](https://jpgoldberg.github.io/toy-crypto-math/why/).

Note that if you need to do cryptography in Python,
I recommend [pyca](https://cryptography.io/) or [PyNaCl](https://pynacl.readthedocs.io/en/latest/).
If you want tools to explore the algebraic and number theoretic constructs used in cryptography,
look at [SageMath](https://doc.sagemath.org/) or [SymPy](https://www.sympy.org/en/index.html).

[published]: https://pypi.org/project/toycrypto/ "toycrypto on PyPi"
[documentation]: https://jpgoldberg.github.io/toy-crypto-math/

[type-badge]: https://github.com/jpgoldberg/toy-crypto-math/actions/workflows/type-check.yml/badge.svg
[lint-badge]: https://github.com/jpgoldberg/toy-crypto-math/actions/workflows/lint.yml/badge.svg
[test-badge]: https://github.com/jpgoldberg/toy-crypto-math/actions/workflows/pytest.yml/badge.svg
[doctest-badge]: https://github.com/jpgoldberg/toy-crypto-math/actions/workflows/doctest.yml/badge.svg
[doc-build-badge]: https://github.com/jpgoldberg/toy-crypto-math/actions/workflows/gh-pages.yml/badge.svg
[codefactor-badge]: https://www.codefactor.io/repository/github/jpgoldberg/toy-crypto-math/badge
zipToCBSA 1.0.1
A small package designed to help convert USPS ZIP Codes to OMB CBSA Codes
Requires Python: >=3.9

# USPS ZIP Code to OMB Core-Based Satistical Area (CBSA)

## Background:
For those unfamiliar, the US Office of Management and Budget (OMB) defines Core Based Statistical Areas (CBSAs) for use by a multitude of other agencies, including the US Dept of Housing and Urban Development.

Meanwhile, most developers and users are not intimately acquainted with the CBSA codes, but may well be accustomed to USPS ZIP Codes.  The OMB publishes "crosswalk files" periodically to help map ZIP codes to CBSAs, and this package serves as an easy means to utilize those without needing to call an API constantly.

This package just has one function, "convert", which takes in a 5-digit USPS ZIP Code and returns all of the info for matching rows in the OMB ZIP-CBSA Crosswalk File, namely CBSA code, USPS ZIP Pref City, USPS ZIP Pref State, and four different ratios of percentage of addresses in a ZIP code mapped to the given CBSA based on designation as residential, business, other, and total addresses.

The return value from this function is a list of dictionaries, each dictionary corresponding to a CBSA associated to the given ZIP code.  If an invalid ZIP code is provided, or the ZIP doesn't map to any CBSA, the function instead returns "FALSE".

For more info on OMB CBSAs, and on how to use the output data, please visit the US HUD portal site: https://www.huduser.gov/portal/datasets/usps_crosswalk.html

## Usage:

```python
from zipToCBSA import convert
results = convert('54499')

for result in results:
    print(result)

```
results
```
{'zip': '54499', 'geoid': 43020, 'res_ratio': 0.817047817047817, 'bus_ratio': 0.9032258064516128, 'oth_ratio': 0.9428571428571428, 'tot_ratio': 0.8233766233766234, 'city': 'WITTENBERG', 'state': 'WI'}
{'zip': '54499', 'geoid': 48140, 'res_ratio': 0.167013167013167, 'bus_ratio': 0.0967741935483871, 'oth_ratio': 0.0571428571428571, 'tot_ratio': 0.1616883116883117, 'city': 'WITTENBERG', 'state': 'WI'}
{'zip': '54499', 'geoid': 44620, 'res_ratio': 0.0138600138600138, 'bus_ratio': 0.0, 'oth_ratio': 0.0, 'tot_ratio': 0.0129870129870129, 'city': 'WITTENBERG', 'state': 'WI'}
{'zip': '54499', 'geoid': 99999, 'res_ratio': 0.002079002079002, 'bus_ratio': 0.0, 'oth_ratio': 0.0, 'tot_ratio': 0.0019480519480519, 'city': 'WITTENBERG', 'state': 'WI'}
```

This code was inspired by the now-defunct zipcode2cbsa at https://github.com/macoj/zipcode2cbsa.
neuralpiano 25.11.41
Hi-Fi neural MIDI piano synthesizer and MIDI renderer
Requires Python: >=3.8
Keywords: MIDI, music, music ai, MIDI piano, Piano synthesizer, renderer, MIDI synthesizer, MIDI renderer, neural synthesizer, artificial intelligence, AI, audio synthesis

# [WIP] Neural Piano
## Hi-Fi neural MIDI piano synthesizer and MIDI renderer

<img width="1024" height="1024" alt="Neural-Piano-Artwork" src="https://github.com/user-attachments/assets/a3d214c0-f88d-4581-9d29-fc26aeec04f8" />

***

## Installation

### pip and setuptools

```sh
# It is recommended that you upgrade pip and setuptools prior to install for max compatibility
!pip install --upgrade pip
!pip install --upgrade setuptools build wheel
```

### pip install

```sh
# The following command will install Neural Piano pip package
# Please note that Neural Piano requires Nvidia GPU with at least 40GB VRAM

!pip install -U neuralpiano
```

***

## Quick-start use example

```python
# Import main Neural Piano module
import neuralpiano

# Render MIDI
neuralpiano.render_midi('input.mid', 'output.wav')
```

***

### Project Los Angeles
### Tegridy Code 2025