semvec/pypi-packages huggingface dataset:
pico-up 0.0.9
Command line tools for developing applications for the Raspberry Pico

# pico-up-and-running

A template repository with some scripts and shared functionality to get up and running with RaspberryPi Pico

## The `pico-up` command

```
python pico-up

free    show the current free ram and rom space, requires a soft-reset
init    initialise a python pico project in the current directory
image   prepare a 128x128 sprite image for use with the pico
push    push local application code to a connected pico
wipe    remove all files from a connected pico
version show the version of pico-up
```

## The `.pico-up.ini` file

This file configures how you want to communicate with and deploy code to the pico.

```ini
[device]
address = '/dev/ttyACM0' # The device id, name or path from mpremote devs

[push]
ignores = # File extensions to ignore when pushing files to the device
        .jpg
        .rgb332
        .bin

modules = # Code modules to push to the device in addition to your code
        pico_up_modules.msgpack_loads
```

For more information on available commands and their effects see [COMMANDS](docs/COMMANDS.md).
FieldEmission 0.1.5
Field Emission Data Postprocessing and Data Provider
Keywords: python,Cold Field Emission,Fowler Nordheim

This packages provides a bunch of classes and methods to handle field emission data (based on files of Field Emission Data AQuisition (FEMDAQ written in C#) measurement tool of the OTH-Regensburg. (GIT-link for the measurement tool may be released in future.)
cformers 0.0.4
SoTA Transformers with C-backend for fast inference on your CPU.
Keywords: python,local inference,c++ inference,language models,cpu inference,quantization

We identify three pillers to enable fast inference of SoTA AI models on your CPU:
1. Fast C/C++ LLM inference kernels for CPU.
2. Machine Learning Research & Exploration front - Compression through quantization, sparsification, training on more data, collecting data and training instruction & chat models.
3. Easy to use API for fast AI inference in dynamically typed language like Python.

This project aims to address the third using LLaMa.cpp and GGML.
bolt-sdk 1.0.0
Bolt Python SDK
Requires Python: >=2.7

# Bolt SDK

This SDK provides an authentication solution for programatically interacting with Bolt. It wraps the boto3 interface so project wide integration is as easy as refactoring `import boto3` to `import bolt as boto3`.

The package affects the signing and routing protocol of the boto3 S3 client, therefore any non S3 clients created through this SDK will be un-affected by the wrapper.

## Prerequisites

The minimum supported version of Python is version 3.

## Installation

```bash
python3 -m pip install bolt-sdk
```

## Configuration

For the client to work it must have knowledge of Bolt's *service discovery url* (`BOLT_URL`)
These are parameterized by the *region* of Bolt's deployment. A preferred *availability zone ID* can also be provided for AZ-aware routing.

**There are two  ways to expose Bolt's URL to the SDK:**
1. Declare the ENV variable: `BOLT_CUSTOM_DOMAIN`, which constructs Bolt URL and hostname based on default naming
```bash
export BOLT_CUSTOM_DOMAIN="example.com"
```

2. Directly declare the ENV variables: `BOLT_URL`
`BOLT_URL` must be formatted as follows:

`https://<subdomain>{region}<domain>`

An example is:

`https://bolt.{region}.google.com`

Where the `{region}` within the URL is a string literal placeholder that will be replaced by the python sdk

```bash
export BOLT_URL="<url>"
```

**There are two ways to expose Bolt's region/preferred availability zone to the SDK:**

1. If running on an EC2 instance the SDK will by default use that instance's region and zone ID
2. With the ENV variables: `AWS_REGION` and `AWS_ZONE_ID`.
```bash
export AWS_REGION='<region>'
export AWS_ZONE_ID='<az-id>'
```

## Debugging

Import the default logger and set its level to DEBUG

`logging.getLogger().setLevel(logging.DEBUG)`


## Tests
Basic integration tests are provided for the modified Session/Client interfaces. They must be run in an environment with a properly configured Bolt deployment accessible.
```bash
python3 tests/tests.py
```
textstats 0.1.2
This is a Pakcage for Extracting Info from the text.
Requires Python: >=3.10,<4.0

TextStats is the package used for analysis of the text.

TextSats gives out various Information for the text provided to it.
Some of them includes :
1. Line Count
2. Word Count
3. Emoji Count
4. Email Count
5. URL Count
6. Proper Punctuation Count
7. ImProper Punctuation Count

Apart from this it also contains NER model i.e spacy to classify the words into named entities.
simple-flatten-json 0.0.9
For flattening complex JSONs
Requires Python: <4,>=3.7

# Usage

simple_json_flatten_py is a Python library for flattening complex JSONs

From

```JSON
{
"a": [ {"b":37},{"c":64}],
"g": 26
}
```
To

```
{
  "a_b":37,
  "a_c":64,
  "g":26
}
```

```Python
from simple_flatten_json_py import simple_flatten_json

# Sample JSON data
data={
   "cat":[
      {
         "fish":"tuna"
      },
      {
         "chicken":3
      },
      {
         "medicine":{
            "pills":5
         }
      }
   ]
}
# returns a flattened json 
# {"cat_0_fish": "tuna",  "cat_1_chicken": 3,"cat_2_medicine_pills": 20}

print(simple_flatten_json.flatten_json(data))
```