semvec/pypi-packages huggingface dataset:
arcanechat-tui 0.12.0
ArcaneChat client for the command line
Requires Python: >=3.10
Keywords: deltachat, ncurses, tui, arcanechat

# ArcaneChat TUI

[![Latest Release](https://img.shields.io/pypi/v/arcanechat-tui.svg)](https://pypi.org/project/arcanechat-tui)
[![License](https://img.shields.io/pypi/l/arcanechat-tui.svg)](https://pypi.org/project/arcanechat-tui)
[![CI](https://github.com/ArcaneChat/arcanechat-tui/actions/workflows/python-ci.yml/badge.svg)](https://github.com/ArcaneChat/arcanechat-tui/actions/workflows/python-ci.yml)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

> a lightweight ArcaneChat client for the command line

<p align="center">
  <img src="https://github.com/ArcaneChat/arcanechat-tui/blob/main/screenshots/e1.png?raw=true" alt="screenshot of ArcaneChat"/>
</p>

ArcaneChat TUI is a ncurses Delta Chat client developed in Python with the urwid library.

#### Features

- [X] Create accounts
- [X] Tweak account configuration (ex. set display name and status)
- [X] Ability to send text messages :)
- [X] Read receipts ✓✓
- [X] Display quoted messages
- [ ] Account switcher
- [ ] Chat operations: delete, pin/unpin, mute/unmute, archive/unarchive, add/remove members, etc.
- [ ] Message operations: reply, delete, open attachment/links, see info, jump to quote
- [ ] Import/export keys and backups
- [ ] Ability to send files
- [ ] Notifications
- [ ] Support for contact verification and group invitations links
- [ ] Block/unblock contacts and see the list of blocked contacts
- [ ] See contact list
- [ ] Search messages and chats
- [ ] Open HTML messages
- [ ] View archived chats

## Installing

#### With pip

This program depends on the `deltachat-rpc-server` program to be installed in your system,
if you want to try to install it using pip run:

```
$ pip install -U "arcanechat-tui[full]"
```

If you will manually install the `deltachat-rpc-server` program in
your PATH on your own, then run:

```
$ pip install -U arcanechat-tui
```

#### With pipx

To install ArcaneChat TUI with [pipx](https://pipx.pypa.io):

```
$ pipx install arcanechat-tui deltachat-rpc-server
```

## Using

After installation the command `arcanechat-tui`
(and `arcanechat` alias) should be available.

For more tips and info check [the user guide](https://github.com/ArcaneChat/arcanechat-tui/blob/main/docs/user-guide.md)
pyacm 1.2
ACM Term Premium
Keywords: asset pricing, yield curve, term premium


[paper_website]: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr340.pdf


# pyacm
Implementation of ["Pricing the Term Structure with Linear Regressions" from 
Adrian, Crump and Moench (2013)][paper_website].

The `NominalACM` class prices the time series and cross-section of the term 
structure of interest rates using a three-step linear regression approach.
Computations are fast, even with a large number of pricing factors. The object 
carries all the relevant variables as atributes:
- The yield curve itself
- The excess returns from the synthetic zero coupon bonds
- The principal components of the curve used as princing factors
- Risk premium parameter estimates
- Yields fitted by the model
- Risk-neutral yields
- Term premium
- Historical in-sample expected returns 
- Expected return loadings


# Instalation
```bash
pip install pyacm
```


# Usage
```python
from pyacm import NominalACM

acm = NominalACM(
    curve=yield_curve,
    n_factors=5,
)
```
The tricky part of using this model is getting the correct data format. The 
`yield_curve` dataframe in the expression above requires:
- Annualized log-yields for zero-coupon bonds
- Observations (index) must be in either monthly or daily frequency
- Maturities (columns) must be equally spaced in **monthly** frequency and start 
at month 1. This means that you need to construct a bootstraped curve for every 
date and interpolate it at fixed monthly maturities


# Examples
Updated estimates for the US are available on the [NY FED website](https://www.newyorkfed.org/research/data_indicators/term-premia-tabs#/overview). 
The file [`example_us`](https://github.com/gusamarante/pyacm/blob/main/example_us.py) reproduces the original outputs using the same 
dataset as the authors.

The jupyter notebook [`example_br`](https://github.com/gusamarante/pyacm/blob/main/example_br.ipynb) 
contains an example application to the Brazilian DI futures curve that 
showcases all the available methods and attributes.

<p align="center">
  <img src="https://raw.githubusercontent.com/gusamarante/pyacm/refs/heads/main/images/DI%20term%20premium.png" alt="DI Term Premium"/>
  <img src="https://raw.githubusercontent.com/gusamarante/pyacm/refs/heads/main/images/DI%20observed%20vs%20risk%20neutral.png" alt="Observed VS Risk Neutral"/>
</p>

# Original Article
> Adrian, Tobias and Crump, Richard K. and Moench, Emanuel, 
> Pricing the Term Structure with Linear Regressions (April 11, 2013). 
> FRB of New York Staff Report No. 340, 
> Available at SSRN: https://ssrn.com/abstract=1362586 or http://dx.doi.org/10.2139/ssrn.1362586

I would like to thank Emanuel Moench for sharing his original MATLAB code in 
order to perfectly replicate these results.

# Citation
> Gustavo Amarante (2025). pyacm: Python Implementation of the ACM Term Premium 
> Model. Retrieved from https://github.com/gusamarante/pyacm
sinapsis-huggingface-hub 0.1.2
Templates for managing Hugging Face models, spaces and datasets.
Requires Python: >=3.10

<h1 align="center">
<br>
<br>
<a href="https://sinapsis.tech/">
  <img
    src="https://github.com/Sinapsis-AI/brand-resources/blob/main/sinapsis_logo/4x/logo.png?raw=true"
    alt="" width="300">
</a>
<br>
Sinapsis Hugging Face Hub
<br>
</h1>

Sinapsis Hugging Face Hub provides a simple and flexible **no-code** implementation of the **Hugging Face Hub** library. It enables users to easily manage models, datasets, and spaces for Hugging Face-related tasks.

<p align="center">
<a href="#installation">🐍 Installation</a> •
<a href="#features">📦 Features</a> •
<a href="#example">▶️ Example usage</a> •
<a href="#documentation">📙 Documentation</a> •
<a href="#license">🔍 License</a>
</p>


<h2 id="installation">🐍 Installation</h2>


Install using your package manager of choice. We encourage the use of <code>uv</code>

Example with <code>uv</code>:

```bash
  uv pip install sinapsis-huggingface-hub --extra-index-url https://pypi.sinapsis.tech
```
 or with raw <code>pip</code>:
```bash
  pip install sinapsis-huggingface-hub --extra-index-url https://pypi.sinapsis.tech
```



> [!IMPORTANT]
> Templates may require extra optional dependencies. For development, we recommend installing the package with all the optional dependencies:
>
with <code>uv</code>:

```bash
  uv pip install sinapsis-huggingface-hub[all] --extra-index-url https://pypi.sinapsis.tech
```
 or with raw <code>pip</code>:
```bash
  pip install sinapsis-huggingface-hub[all] --extra-index-url https://pypi.sinapsis.tech
```

<h2 id="features">📦 Features</h2>

The templates in this package include functionality to:

- **HuggingFaceDownloader**: Downloads a repository snapshot from the Hugging Face Hub.

<h2 id="example">▶️ Example Usage</h2>

Below is an example YAML configuration for running a **Stable Diffusion Downloader** pipeline using Sinapsis.

<details>
<summary ><strong><span style="font-size: 1.4em;">Config</span></strong></summary>

```yaml
agent:
  name: stable_diffusion_agent_downloader

templates:
- template_name: InputTemplate
  class_name: InputTemplate
  attributes: {}

- template_name: HuggingFaceDownloader
  class_name: HuggingFaceDownloader
  template_input: InputTemplate
  attributes:
    repo_id: stable-diffusion-v1-5/stable-diffusion-v1-5
    max_workers: 4
```
</details>

To run the config, use the CLI:
```bash
sinapsis run name_of_config.yml
```

<h2 id="documentation">📙 Documentation</h2>

Documentation is available on the [sinapsis website](https://docs.sinapsis.tech/docs)

Tutorials for different projects within sinapsis are available at [sinapsis tutorials page](https://docs.sinapsis.tech/tutorials)

<h2 id="license">🔍 License</h2>

This project is licensed under the AGPLv3 license, which encourages open collaboration and sharing. For more details, please refer to the [LICENSE](LICENSE) file.

For commercial use, please refer to our [official Sinapsis website](https://sinapsis.tech) for information on obtaining a commercial license.