semvec/pypi-packages huggingface dataset:
printclean 1.1.0
Cleans photographed documents for improved printing
Requires Python: >=3.9

# print-clean

This script helps prepare photographed documents for printing:

Before:

![before](https://github.com/danmysak/print-clean/raw/main/demo/demo_1.jpg)
![before](https://github.com/danmysak/print-clean/raw/main/demo/demo_2.jpg)

After:

![after](https://github.com/danmysak/print-clean/raw/main/demo/demo_1-cleaned-threshold.png)
![after](https://github.com/danmysak/print-clean/raw/main/demo/demo_2-cleaned-gauss_blur.png)

## Installation

Install [Python](https://www.python.org/downloads/) 3.9 or higher, then run:

```
pip install printclean
```

or

```
pip3 install printclean
```

## Usage

```
printclean <path-to-image> [<path-to-another-image> ...] [--method threshold|gauss_blur] [--level value] [--lang <code1> <code2> ...]
```

You can provide one or more paths to image files. The script will clean them up and save the results in the same directory.

You can specify the cleanup method using the `--method` parameter. The available methods are `threshold` and `gauss_blur`, with the default being `gauss_blur`.

If the `--method` is set to `threshold`, you can optionally specify the threshold value for cleanup using the `--level` parameter. The value must be an integer between 0 and 255 (inclusive). Larger values produce images with fewer filled pixels. The default value is 10.

Additionally, you may specify the language(s) of your document using the `--lang` parameter. This can help the script improve perspective correction for the input image. Note that the script currently supports only simple rotations for perspective correction.

You can find the available languages and their corresponding codes [here](https://tesseract-ocr.github.io/tessdoc/Data-Files-in-different-versions.html). Depending on your system, you may need to install the required language files for the Tesseract OCR engine: ([Windows](https://stackoverflow.com/a/69958671/430083), [Ubuntu](https://askubuntu.com/a/798492/1064838), [Mac OS](https://stackoverflow.com/a/60595075/430083)).

The output image will be saved as a PNG file with the suffix `-cleaned`.
dvtm-utils 0.1.0
Utils package for loading datasets and models
Requires Python: >=3.6

# mlutils

Function utils to load work with datasets and models

## how to use it
clone the repository into your folder
```python
cd myproject
git clone https://github.com/nluninja/mlutils.git
```
import the module into your project, and call the needed utils

```python
import mlutils
```



## utils list

### model utils
Utils to work with models.

```python
compute_prediction_latency(dataset, model, n_instances=-1):
    """Compute prediction latency of a model.

def from_encode_to_literal_labels(y_true, y_pred, idx2tag):
    '''Transform sequences of encoded labels in sequences of string labels'''
```

### keras utils
Utils to load embeddings, to create LSTMs, and to memory usage: the memory functions work with Tensorflow only

```python
def get_model_memory_usage(batch_size, model):
    """Return memory usage of a model in MB given the batch size"""

def print_model_memory_usage(batch_size, model):
    """Print memory usage of a model in MB given the batch size"""

def load_glove_embedding_matrix(path, word_index, embed_dim):
    """Load Glove embeddings.    """

def load_w2v_nlpl_embedding_matrix(path, word_index, embed_dim):
    """Load NLPL Italian embedding."""

def create_BiLSTM(vocabulary_size, seq_len, n_classes, hidden_cells=128, 
                  embed_dim=32, drop=0.5, use_glove=False, glove_matrix=None):
    """Create a BiLSTM model using keras, given its parameters"""

def create_paper_BiLSTM(vocabulary_size, seq_len, n_classes, hidden_cells=200, 
                  embed_dim=100, drop=0.4, use_glove=False, glove_matrix=None):
    """Create a BiLSTM model using keras, given its parameters"""

def remove_flat_padding(X, y_true, y_pred, pad=0):
    """Remove padding predictions and flatten the list of sequences"""

def remove_seq_padding(X, y_true, y_pred, pad=0):
    """Remove padding predictions from list of sequences"""
```

### I/O utils
Utils to load datasets such as conll, wikiner.

```python
def open_read_from_url(url):
    """ Take in input an url to a .txt file and return the list of its raws"""

def read_raw_conll(url_root, dir_path, filename):
    """Read a file which contains a conll03 dataset"""

def is_real_sentence(only_token, sentence):
    """Chek if a sentence is a real sentence or a document separator"""

def load_conll_data(filename, url_root=CONLL_URL_ROOT, dir_path='', 
                    only_tokens=False):
    """ Take an url to the raw .txt files that you can find the repo linked above,
    load data and save it into a list of tuples data structure. """

def _df_to_xy(df):
    """Transform anerd dataframe in X, y sets."""

def load_wikiner(path, token_only=False):
    """Load WikiNER dataset."""

def _get_digits(text):
    """Preprocess numbers in tokens accordingly to itWac word embedding."""

def _normalize_text(word):
    """Preprocess word in order to match with the itWac embedding vocabulary"""

def itwac_preprocess_data(sentences):
    """Preprocess text in order to match with the itWac embedding vocabulary"""
```
baikalctl 1.2.0
Top-level package for baikalctl.
Keywords: baikalctl

baikalctl
=========


![Image](https://img.shields.io/github/license/rstms/baikalctl)

![Image](https://img.shields.io/pypi/v/baikalctl.svg)





Baikal caldav/carddav server admin cli


* Free software: MIT license
* Documentation: https://baikalctl.readthedocs.io.



Credits
-------

This package was created with Cookiecutter and `rstms/cookiecutter-python-cli`, a fork of the `audreyr/cookiecutter-pypackage` project template.

[audreyr/cookiecutter](https://github.com/audreyr/cookiecutter)
[audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage)
[rstms/cookiecutter-python-cli](https://github.com/rstms/cookiecutter-python-cli)
viznvsmi 0.0.1
NVSMI plugins for VizTracer
Requires Python: >=3.8

# VizNvsmi
[VizTracer](https://github.com/gaogaotiantian/viztracer) plugins for NVSMI.
It is inspired by the official VizTracer plugings: [vizplugins](https://github.com/gaogaotiantian/vizplugins).

# Features
VizNvsmi currently only supports NVIDIA metrics tracking via [nvidia-smi](https://docs.nvidia.com/deploy/nvidia-smi/index.html).

# Install
The preferred way to install VizNvsmi is via pip:

```sh
pip install viznvsmi
```

# Basic Usage
VizNvsmi should be used with VizTracer.

You can use VizNvsmi and the plugin via command line:

```sh
viztracer --plugin viznvsmi.utilization.memory -i 0 -- my_script.py arg1 arg2
```

Or equivalent syntax:

```sh
viztracer --plugin "viznvsmi --utilization.memory -i 0" -- my_script.py arg1 arg2
```

You can also add the plugin to VizTracer programmatically as described in the [official documentation](https://viztracer.readthedocs.io/en/latest/plugins.html).

Finally, VizNvsmi provides a [PyTorch Lightning Callback](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html) that can be used as part of model training.
qiboconnection 0.23.2
Python interface to Qilimanjaro's Services for quantum job executions.
Requires Python: >=3.10.0

# Qiboconnection

[![codecov](https://codecov.io/gh/qilimanjaro-tech/qiboconnection/graph/badge.svg?token=VBR7YDSZW5)](https://codecov.io/gh/qilimanjaro-tech/qiboconnection)
[![Documentation Status](https://readthedocs.org/projects/qiboconnection/badge/?version=latest)](https://qaas.readthedocs.io/projects/qiboconnection/en/latest/?badge=latest)

Python library that allows you to execute quantum programs on Qilimanjaro's QPUs and simulators.

## Contributions

Thank you for your interest in our project. While we appreciate your enthusiasm and interest in contributing, we would like to clarify our policy regarding external contributions.

### Our Contribution Policy

This project is primarily intended for reference purposes, and we do not actively accept or manage external contributions, including pull requests and issue reports. Our development team maintains this codebase for internal use and does not have the capacity to review or merge contributions from the community.

### Why We Have This Policy

Our decision to limit external contributions is based on our specific project goals, resource constraints, and internal policies. While we understand the value of collaboration and open-source contributions, we have chosen to maintain this project as a reference rather than a collaborative, community-driven effort.

### Seeking Help and Support

If you have questions about using this project or encounter issues, please feel free to open an issue in the repository for discussion. However, please be aware that our ability to provide support or address issues may be limited.

Thank you for your understanding and for considering our project. We hope that you find it useful for your needs, and we wish you the best in your open-source endeavors.

Sincerely,

Qilimanjaro Quantum Tech