semvec/pypi-packages huggingface dataset:
aesplot 1.1
Aesthetics and Framework
Requires Python: >=3.0, <4
Keywords: R,statistics,latex,html,aesthetics


===============================================================
Aesthetic for Matplotlib and Python Framework for data analysis
===============================================================

Aesthetics
==========

To use only the aesthetics and not the framework simply use create a class instance and call for PlotLikeR().plot_cfg() and/or PlotLikeR().plot_cfg_grid() methods.

In the matplotlib documentation it is possible to find a description of each individual parameter. The majority of which, in the Aesplot library, are set as the default. For more check this link: https://matplotlib.org/stable/tutorials/introductory/customizing.html#the-default-matplotlibrc-file .

Framework
=========

Use a separate python file to create the basic framework structure.
Modify the new file or string to make your analysis.

Utils
=====

Fixing the SVG export from matplotlib
-------------------------------------

When saving the matplotlib figure as SVG, the syntax used doesn't contain any &lt;text&gt;&lt;/text&gt;. Instead, matplotlib opts for creating there own letters as mathematical curves. This can be an issue due to the "low resolution" aspect of these curves. To solve this, an utility tool was developed to find and replace the curves for letters.

Additionally, when using LaTex equations in labels and/or legends, some elements can also be converted into HTML syntax.

Converting LaTex to HTML
------------------------

Some mathematical notation is usually used when plotting graphs such as square root, superscript, index and many others. And, since matplotlib supports LaTex interpretation/compilation, it is useful to have a translation from one markup language to another.

Using utils.SVGText() replaces markups, such as /textbf{}, into an equivalent &lt;tspan&gt;&lt;/tspan&gt; around the tag. More LaTex markups will be added in the future.

Dependencies
------------

  - Numpy: https://numpy.org
  - Matplotlib: https://matplotlib.org
  - Pandas: https://pandas.pydata.org
  - Scipy: https://scipy.org
  - Miktex: https://miktex.org or other latex compiler

To-dos
------

  - Expand the latex functions that can be interpreted and translated
  - Develop tests
  - Create data examples and plot examples
py-leap-api 0.0.6
Unofficial Library to interact with TryLeap REST API
Requires Python: >=3.0.0, <4
Keywords: Leap,api,genrative,image

# TryLeap Python API
The py_leap_api Python package provides a simple interface for interacting with the TryLeap service, a cloud-based platform for generating images using artificial intelligence.

## Installation
You can install the py_leap_api package using pip:

```bash
pip install py_leap_api
```

## Getting Started
First, you'll need to create an account on the TryLeap website and obtain an API key.

Once you have an API key, you can use the TryLeap class to interact with the service. Here's a simple example that creates a model, uploads some images, and generates some images based on a prompt:

```python
from py_leap_api.leap import TryLeap

# Create a TryLeap object with your API key
api_key = "your-api-key"
leap = TryLeap(api=api_key)

# Create a model
model = await leap.create_model("My Model")
print(model)

# add the model id
leap.set_model(model=model["id"])

# Upload some images
urls = [
    "https://example.com/image1.png",
    "https://example.com/image2.png",
    "https://example.com/image3.png",
]
await leap.upload_images_url(urls)

# Train the model
queue = await leap.training_model()
print(queue)
```

Ideally if you have a webhook url you can provide it to training_model in order to know when the training finished.
Otherwise you can fetch the status that return from the function.

```python
# Generate some images
prompt = "a cat sitting on a couch"
response = await leap.generate_image(prompt, number_images=3)

# Retrieve the output images
output_images = await leap.output_images()
```

# License
This library is licensed under the MIT License. See the LICENSE file for details.
numerai-compute-lite 0.0.2
Quick and easy way to deploy your Numerai models
Requires Python: >=3.7

# compute-lite

# build and release
```
pip install -r requirements.txt
# modify pyproject.toml version
python -m build  # this will generate dist dir
python -m twine upload dist/*  # upload to pypi
```

# usage
```python
import json
import os
import pandas as pd
import numerai_compute_lite as ncl
from numerapi import NumerAPI
from lightgbm import LGBMRegressor

import dotenv
dotenv.load_dotenv() # loads API secrets from .env file

napi = NumerAPI()

napi.download_dataset("v4/train.parquet")
napi.download_dataset("v4/features.json")
training_data = pd.read_parquet('v4/train.parquet')

feature_set = []
with open("v4/features.json", "r") as f:
    feature_metadata = json.load(f)
features = feature_metadata["feature_sets"]["small"]

model = LGBMRegressor()
model.fit(
    training_data[features],
    training_data['target']
)

targets = training_data.columns.str.startswith('target')

model_id = '08e77bbf-036c-4216-b2f7-f8ed4beb88e9'
ncl.deploy(model_id, model, features, 'requirements.txt')
```
ApifonicaClient 1.0.1
Unofficial Python Package to easily integrate the Apifonica API to your project

# apifonica-client
Unofficial Python Package to easily integrate the [Apifonica](https://www.apifonica.com/) API to your project.

> Note: Currently this only supportings sending SMS messages. Other api endpoints will be implemented over time.


## Installation

apifonica-client is available on PyPi:

```
$ python -m pip install ApifonicaClient
```


## Usage

```
from apifonica_client import ApifonicaClient

apifonica = ApifonicaClient(
    apifonica_account_sid="your_account_sid_here",
    apifonica_auth_token="your_auth_token_here",
    apifonica_number_from="your_phone_number_here"
)

apifonica.send_sms("1234567890", "Hello, world!")
```