semvec/pypi-packages huggingface dataset:
xia-user 0.1.33
xia-user
Requires Python: >=3.9

-.. image:: https://img.shields.io/pypi/v/xia-user.svg?color=blue
-   :alt: PyPI-Server
-   :target: https://pypi.org/project/xia-user/
+Introduction
+=============================
 
-====================================
-X-I-A
-====================================
+Welcome to use X-I-A's software
 
 
-Quick start
+
psusannx-email 0.1.4
A package with a function for sending emails easily using sendgrid.

# psusannx_email

A package that allows emails to be sent through a python function using [Sendgrid](https://www.sendgrid.com/), provided a valid [Sendgrid API Key](https://docs.sendgrid.com/ui/account-and-settings/api-keys#creating-an-api-key) is passed as an argument to the function (`sendgrid_api_key`).

The docs for using Sendgrid with python can be found [here](https://docs.sendgrid.com/for-developers/sending-email/quickstart-python).

This package was created to be used as a subpackage in a wider project - PSUSANNX.

## Package Functions

- send_email()

## Installation

```python
pip install psusannx-email
```

## Usage

```python
# Import the function from the package
from psusannx_email import send_email

# Get some info about the function
help(send_email) 
```

Now use the function to send an email.

```python
# Send a test email from your own personal email (once you have a sendgrid api key)
send_email(
    subject="Test email using the psusannx_email package", 
    body="This is a test email.\n\nThanks.",
    from_email="<your-email>",
    recipients=["<recipient-1>", "<recipient-2>"],
    sendgrid_api_key="<verified-sendgrid-api-key>"
)
```

## Notes

- The package is quite restricted in what it can do, but it only needs to do things that are required by the parent project so there won't be much development.
- A new feature that will be added is the ability to send emails where the body is html, rather than just plain text.
noun-splitter 0.0.1
Python module to decompose nouns based on the SECOS algorithm
Requires Python: >=3.6

# SECOS
This repo is a modular python implementation of the SECOS algorithm for decomposing composite nouns.

Based on the SECOS algorithm:

[original python implementation](https://github.com/riedlma/SECOS)

[original paper](https://www.inf.uni-hamburg.de/en/inst/ab/lt/publications/2016-riedletal-naacl.pdf)

However, the training data of the models have been distilled slightly to reduce the size of the models. More information on this can be found in the pretrained-models directory in the github repo. Typically though it involves trimming out words with low frequency counts, words with non-unicode characters etc. 

# Installation

## From Github
`pip install git+https://github.com/mhaugestad/noun-splitter.git -U`

## From Source
```
git clone
cd noun-splitter
pip install -e . -U
```

## From Pip
```
pip install noun-splitter
```

## Installing models:
The module relies on pretrained models to be passed in. These can be downloaded from command line as follows:

`python -m secos download --model de`

The command line tool also takes an optional argument --overwrite. This is to be used if you would like to redownload a model for whatever reason, as follows:

`python -m secos download --model no --overwrite`

Alternatively, you can download models directly from a python script or notebook like this:

```
from secos import Decomposition

Decomposition.download_model('de')
```

Available models and their names are:

| Language  | Model |
| ------------- | ------------- |
| Danish | da |
| German | de |
| English | en |
| Spanish | es |
| Estonian | et |
| Finnish | fi |
| Hungarian | hu |
| Latin | la |
| Latvian | lv |
| Netherland | nl |
| Norway | no |
| Swedish | sv |

# Basic Usage
```
from secos import Decomposition

model = Decomposition.load_model('de')

secos = Decomposition(model)

secos.decompose("Bundesfinanzministerium")

['bundes', 'finanz', 'ministerium']
```

# Evaluation
The evaluation folder in the github repo includes code for the evaluation of the pretrained models.
pyfmrheo 0.1.3
A python package to perform analysis of Force Microscopy data.
Requires Python: >=3.6

# Pyfmrheo

In development, not fully stable.
If you find any issues please open an issue at:
https://github.com/jlopezalo/PyFMRheo/issues

If you have any questions, contact:
jla.lopez.18@gmail.com

## Installation
```
pip install pyfmrheo
```

## Usage
In the examples folder you can find several Jupyter notebooks showing how
to use the routines implemented in the library.
If you would like any other examples to be added do not hesitate to open an issue.
tasmota-metrics 0.4.3
Firmware size analysis for ESP-IDF
Requires Python: >=3.7
Keywords: espressif,embedded,project,size


# tasmota-metrics is based on esp-idf-size

The ```esp-idf-size``` package is part of the [ESP-IDF](https://github.com/espressif/esp-idf) SDK for Espressif products and is automatically installed.

It is responsible for analyzing firmware binary size of ESP-IDF projects.