semvec/pypi-packages huggingface dataset:
fixpoint-python-sdk 0.0.1
Python SDK for Fixpoint - Auto-improvement to make your LLM apps smarter
Requires Python: >=3.8

# Fixpoint Python SDK

The `FixpointClient` wraps the OpenAI API Client. You can call it just like OpenAI API Client. The sdk will intercept calls to certain OpenAI APIs, record input / outputs and forward that information to Fixpoint's api server.

## Usage

To use the sdk make sure that you have the following variables set in your environment: `FIXPOINT_API_KEY` and `OPENAI_API_KEY`.

## Virtual Env

To create a virtual environment called `venv` from your terminal run `python3 -m venv venv`.

### Activate

`source venv/bin/activate`

### Install packages

To install packages from `requirements.txt` in your virtual environment using `pip` run: `pip install -r requirements.txt`

### Deactivate

`deactivate`

## Examples

You can find examples of how to use the API in `examples`.
dcre 2023.2.4
Python package for CRE processing
Requires Python: >=3.9

# dCRE
Computation and decomposition of Cloud Radiative Effects (CRE)

![ci.yaml](https://github.com/observingClouds/dCRE/actions/workflows/ci.yaml/badge.svg)

> [!WARNING]
> This package is still under development. Things will break.

## Installation
```
pip install dcre
```

## Usage
```python
import dcre

# Add reference and perturbed sources
src1 = dcre.cre_decomposition.Simulation(CWP, CF, NCCN, albedo, albd_clear,SOLIN)
src2 = dcre.cre_decomposition.Simulation(CWP, CF, NCCN, albedo, albd_clear,SOLIN)

# Configure decomposition
decompositer = dcre.cre_decomposition.CRE_Decompositer(src1, src2)

# Decompose
decompositer.decompose(cre_scaling=False)

# Write results to disk
ds = decompositer.to_dataset()
ds.to_netcdf("decomposition.nc")
```
thehelp 0.0.0
Helpers for use with the rich package.
Requires Python: <3.12,>=3.10
Keywords: rich,shortcuts

# thehelp

Helpers for use with the [rich package](https://pypi.org/project/rich/).  

Type less when applying multiple colors to f-strings,
access all color names with your IDE's autocomplete,
and easily apply color gradients to text.

## Installation

Install with:

<pre>
pip install thehelp
</pre>



## Usage

The `Tag` class is essentially a wrapper to shorten using `rich` tags.  
When a `Tag` is casted to a string it is formatted with surrounding square brackets 
and the `o` or `off` properties can be accessed to return the matching closing tag.  

<pre>
from thehelp import Tag
p = Tag("pale_turquoise4")
c = Tag("cornflower_blue")
s = f"{p}This{p.o} {c}is{c.o} {p}a{p.o} {c}string"
</pre>

is equivalent to

<pre>
s = "[pale_turquoise4]This[/pale_turquoise4] [cornflower_blue]is[/cornflower_blue] [pale_turquoise4]a[/pale_turquoise4] [cornflower_blue]string"
</pre>

---
The `ColorMap` class contains two `Tag` properties for each 
[named color](https://rich.readthedocs.io/en/latest/appendix/colors.html) 
(except shades of grey, those only have a full name property):
one that's the full name of the color and one that's an abbreviated name, for convenience.  

This is useful for seeing color options using autocomplete:
![](imgs/autocomplete.png)

The class also supports iterating over the tags as well as selecting random colors:
![](imgs/iteration.png)

---
The `Gradient` class can be used to easily apply a color sweep across text:
![](imgs/gradient.png)