semvec/pypi-packages huggingface dataset:
ssm-jsonrepair 3.8.2
JSON repair can solve multiple problems in json
Requires Python: >=3.8

# Json Repair

The project is created from https://github.com/josdejong/jsonrepair project, which is a Javascript based solution for repairing bad or incomplete json data.
It has been ported as python class.

The following issues can be fixed:

* Add missing quotes around keys
* Add missing escape characters
* Add missing commas
* Add missing closing brackets
* Repair truncated JSON
* Replace single quotes with double quotes
* Replace special quote characters like “...” with regular double quotes
* Replace special white space characters with regular spaces
* Replace Python constants None, True, and False with null, true, and false
* Strip trailing commas
* Strip comments like /* ... */ and // ...
* Strip ellipsis in arrays and objects like [1, 2, 3, ...]
* Strip JSONP notation like callback({ ... })
* Strip escape characters from an escaped string like {\"stringified\": \"content\"}
* Strip MongoDB data types like NumberLong(2) and ISODate("2012-12-19T06:01:17.171Z")
* Concatenate strings like "long text" + "more text on next line"
* Turn newline delimited JSON into a valid JSON array, for example:
```
{ "id": 1, "name": "John" }
{ "id": 2, "name": "Sarah" }
```

# Usage:
```
from ssm_jsonrepair import JsonRepair

data = '[[{"$match":{"agent.name:{"$exists":1}}}]]'
jr = JsonRepair()
corrected = jr.jsonrepair(data)
print(corrected)
```

# Error handling

In case of error there are 2 options:
* The program can correct the error => the corrected json is the result.
* The error cannot be repaired => JSONRepairError exception occurs.
atsphinx-sqlite3fts 0.1.4
Sphinx document searcher using SQLite3.
Requires Python: >=3.7

===================
atsphinx-sqlite3fts
===================

Power search for Sphinx by SQLite3-FTS extension.

.. image:: https://img.shields.io/pypi/v/atsphinx-sqlite3fts.svg
    :target: https://pypi.org/project/atsphinx-sqlite3fts/

.. image:: https://github.com/atsphinx/sqlite3fts/actions/workflows/main.yml/badge.svg?branch=main
   :alt: Run CI
   :target: https://github.com/atsphinx/sqlite3fts/actions/workflows/main.yml

.. image:: https://readthedocs.org/projects/atsphinx-sqlite3fts/badge/?version=latest
    :target: https://atsphinx-sqlite3fts.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

.. note:: This is experimental library

Overview
========

This is sphinx extension to provide search component with full-text search database.

When ``sphinx-build`` runs with this extension, builder generate these components.

* SQLite database with FTS extension
* Records of all documents
* Search page HTML with sql.js

This will be useful when you want to embed strong full-text search with keeping static-site structure.

Installation
============

.. code-block:: console

   pip install atsphinx-sqlite3fts

Usage
=====

1. Register extension into your ``conf.py`` and configure it.

.. code-block:: python

   extensions = [
       #
       # Other extensions
       #
       "atsphinx.sqlite3fts",  # Add it
   ]

2. Run builder (html-based builder only).

.. code-block:: console

   sphinx-build -M html source build

3. To try it in local, use ``http.server`` module.

.. code-block:: console

   python -m http.server -d build

  Please access http://localhost:8000/search.html
santali-textts 0.0.4
Convert Olchiki text into speech (Beta version)
Requires Python: >=3.6

# Text-to-Speech Program

## Overview

This is a simple Text-to-Speech (TTS) program developed in Python. Currently, it has been trained on a limited set of words, and it's designed to convert text input into speech output.

## Features

- Converts text to speech using a basic trained model.
- Limited vocabulary at present; additional training is required for broader language support.

## Requirements

- Python 3.8
- Internet

## Installation

```
pip install santali_textts
```

## Usage

1. **Train Text:**

   Custom Vocabulary: Currently trained with a limited set of words, including: ᱟᱵᱩ, ᱵᱟᱝ, ᱫᱚᱱ, ᱫᱟᱨ, ᱦᱟᱨᱩᱵ, ᱦᱮᱡᱳ, ᱦᱮᱱᱤ, ᱡᱟᱯᱮᱰ, ᱡᱚᱢ, ᱡᱳᱛᱳ, ᱠᱳᱢ, ᱞᱟᱱᱟ, ᱢᱟᱨᱟᱱᱜ, ᱢᱩᱪᱟ, ᱟᱪᱦᱳᱨ, ᱟᱫᱤ, ᱟᱠᱟᱱ, ᱟᱨ, ᱵᱳᱡᱳ, ᱵᱩᱨᱩ, ᱪᱤᱴᱟᱱ, ᱫᱩᱞᱟᱨᱤ, ᱜᱟᱫᱭᱟ, ᱠᱟᱢᱤ, ᱠᱟᱱᱟ, ᱢᱤᱫᱰᱳᱣ, ᱢᱤᱫᱴᱮ, ᱳᱠᱴᱮ, ᱯᱟᱱᱡᱟ, ᱨᱟᱯᱩ, ᱨᱮᱟᱠ, ᱨᱟᱥᱠᱟ, ᱥᱟᱠᱟᱢ, ᱛᱟᱞᱟᱨᱮᱱ, ᱛᱳᱵᱤ, ᱵᱦᱮᱛᱨᱮ.

2. **Run the Program:**

    ```bash
    from santali_textts import santali_audio

    text = "ᱟᱵᱩ ᱵᱟᱝ ᱫᱚᱱ ᱫᱟᱨ ᱦᱟᱨᱩᱵ ᱦᱮᱡᱳ ᱦᱮᱱᱫᱤ ᱡᱟᱯᱮᱰ ᱡᱚᱢ ᱡᱳᱛᱳ ᱠᱳᱢ ᱞᱟᱱᱫᱟ ᱢᱟᱨᱟᱱᱜ ᱢᱩᱪᱟ"
    output_filename = "output_audio.wav"
    santali_audio(text, output_filename)

    ```

3. **Play the Output:**

   The output will be saved as an audio file in the specified output directory. You can play it using any standard audio player.

## Contributing

Feel free to contribute to the project by submitting pull requests or opening issues. If you have suggestions for improvements or additional features, your input is welcome!
gdbdbg 0.2.5
gdb debugger
Requires Python: >=3.10

gdbdbg
======

gdbdbg will gather some general debugging information as well as the stack
traces from each thread.

```
gdbdbg-info <pid>
```

gdbdbg-inject can be used to inject some python code into the running process.

If you have manhole installed you can write a script like this:

```manhole.py
import manhole
manhole.install()
```

And inject it with gdbdbg-inject:

```
gdbdbg-inject <pid> manhole.py
```
meu-primeiro-pacote.1 0.0.2
Pacote de testes da aula de criação de pacote de imagens com Python.
Requires Python: >=3.1

# meu_primeiro_pacote

Description. 
The package meu_primeiro_pacote is used to:
	- Histogram matching
	- Structural similarity
	- Resize image
Utils:
	- Read image
	- Save image
	- Plot image
	- Plot result
	- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install meu_primeiro_pacote

```bash
pip install meu_primeiro_pacote

```

## Author
Jefferson

## License
[MIT](https://choosealicense.com/licenses/mit/)
davodtime 0.1.2
A command line tool to display time in various formats.
Requires Python: >=3.6

# DavodTime CLI

A command line application (designed with the help of ChatGPT) designed to
produce timestamps in the format `MMddYYYY-HHmmss` across timezones.

```troff
.TH DAVODTIME 1 "August 2024" "Version 0.1.0" "User Commands"

.SH NAME
davodtime \- A command-line tool to display time in various formats.

.SH SYNOPSIS
.B davodtime
[\fButc\fR | \fIGMT\fR | \fITZ\fR | \fB-h\fR | \fB--help\fR]

.SH DESCRIPTION
.B davodtime
is a command-line utility that displays the current time in various time zones or formats.

.SH OPTIONS
.TP
.B -h, --help
Displays this help message and exits.

.TP
.B utc
Displays the current UTC (Coordinated Universal Time) in the format MMddYYYY-HHmmss.

.TP
.B TZ
Any standard timezone abbreviation (e.g., GMT, PST, EST) can be provided as an argument to display the current time in that timezone, in the format MMddYYYY-HHmmss.

.TP
If no arguments are provided, the local system time is displayed in the format MMddYYYY-HHmmss.

.SH EXAMPLES
.TP
.B davodtime
Displays the local system time.

.TP
.B davodtime utc
Displays the current time in UTC.

.TP
.B davodtime pst
Displays the current time in Pacific Standard Time.

.TP
.B davodtime -h
Displays the help message.

.SH AUTHOR
Your Name <your.email@example.com>

.SH LICENSE
This software is licensed under the MIT License.

.SH SEE ALSO
.B date(1)
```
PDNPR 0.1.1
Tool to find allosteric route based on MD files
Requires Python: >=3.9
Keywords: Python, Protein, allosteric, Network, Shortest route, windows, mac, linux


# Protein Dynamic Network Pathway Runner (PDNPR)

PDNPR is a tool for visualizing protein dynamic network paths, combining libraries such as PyMOL, NetworkX and MDTraj to achieve trajectory extraction, network construction, path analysis and visualization from molecular dynamics.

<!-- ## Code get
```sh
git clone https://github.com/Spencer-JRWang/PDNPR
```

## Environment configuration

### Dependency package
Create and configure the required environment using Conda.


### Create Conda environment:
- Build environment
```sh
conda env create -f environment.yml
```

- Activate environment
```sh
conda activate PDNPR -->
<!-- ``` -->

## Run PDNPR
1. Call PDNPR:

- use PDNPR GUI
```python
from PDNPR import GUI
```

- use PDNPR package
```python
from PDNPR.PDNPR import pdnpr
pdnpr(step, start_AA, end_AA, edge_cutoff, md_file, pdb_file)
```

2. Set parameters
- On the GUI screen, enter the following parameters:
  - Step: retrieves the frame stride.
  - Start Amino Acid: indicates the start amino acid number.
  - End Amino Acid: indicates the number of the end amino acid.
  - Edge Cutoff: specifies the threshold of the edge weight.
  - Select file
  - Click the run button to select the Molecular Dynamics trajectory file (XTC file) and Protein structure file (PDB file).

- Run the task
  - The output area displays progress and information. 
  - The task consists of the following steps:
    - Extract frames
    - Generating network
    - Merge networks
    - Calculate the shortest path
    - Generate and save PyMOL images
  - View results
  - After completion of the task, the output area will display the information of the shortest path, save the image and pse file, and automatically open the generated image file.

<!-- ## Running example
## GUI
<p align="center">
  <img src="https://github.com/Spencer-JRWang/PDNPR/blob/main/Example/Output/run.png" alt="Figure_run" width="300" />
</p>

### Shortest route
```txt
shortest route: 915 -> 936 -> 935 -> 809 -> 808 -> 840 -> 841 -> 709 -> 708 -> 747 -> 743 -> 88
```

### PyMoL Figure
<p align="center">
  <img src="Example/Output/pymol_fig.png" alt="Figure_mol" width="500" />
</p>

## package
```python
from PDNPR import pdnpr
pdnpr(step, start_AA, end_AA, edge_cutoff, md_file, pdb_file)
``` -->
exinc 1.0.3
Tool to expand C++ includes targeting competitive programmers.
Requires Python: <4.0,>=3.10

# Exinc

Tool to expand C++ includes for competitive programmers.

## Installation

> From `pip`:
> ```console
> $ pip install exinc
> ```

> From source:
> ```console
> $ pip install .
> ```

## Configuration

Run the tool one first time for it to create a default configuration.

```console
$ exinc --help
```

Now you'll find a configuration file in `/home/$USER/.exinc` that you can edit.

## Usage

First, you have to properly place the files you want to include. You should either:

1. Put them in the same folder of your to-be-compiled source code;
2. Or put them somewhere, and add such path to `DEFAULT_PATHS` in the `.exinc` configuration file.

Notice that when you go with (2), you also have to make sure you add this path to other places of interest. For instance, when using `vscode`, you also want to make sure IntelliSense will recognize these headers.

Now, you can write C++-based solutions and use your newly available includes:

```cpp
// MyIncludedCode.cpp
int included_function() {
  return 42;
}
```

```cpp
// solution.cpp
#include "MyIncludedCode.cpp"
#include <bits/stdc++.h>

int32_t main() {
  cout << included_function() << endl;
  // Code should compile, and print 42.
}
```

Ultimately, this code will be expanded by Exinc to:

```cpp
// solution.pre.cpp
int included_function() {
  return 42;
}
#include <bits/stdc++.h>

int32_t main() {
  cout << included_function() << endl;
  // Code should compile, and print 42.
}
```
openthread-otci 0.0.1
OpenThread Control Interface
Requires Python: >=3.6

# OpenThread Control Interface

The OpenThread Control Interface (OTCI) is a library which provides uniform python interfaces to connect and control various kinds of devices running OpenThread.

## Supported device types

- OpenThread CLI
  - SOC device via Serial
- OpenThread NCP (limited support via [pyspinel](https://pypi.org/project/pyspinel/))
  - SOC device via Serial
- [OpenThread Border Router](https://github.com/openthread/ot-br-posix)
  - OTBR device via SSH

## Example

```python
import otci

# Connect to an OTBR device via SSH
node1 = otci.connect_otbr_ssh("192.168.1.101")

# Connect to an OpenThread CLI device via Serial
node2 = otci.connect_cli_serial("/dev/ttyACM0"))

# Start node1 to become Leader
node1.dataset_init_buffer()
node1.dataset_set_buffer(network_name='test', network_key='00112233445566778899aabbccddeeff', panid=0xface, channel=11)
node1.dataset_commit_buffer('active')

node1.ifconfig_up()
node1.thread_start()
node1.wait(5)
assert node1.get_state() == "leader"

# Start Commissioner on node1
node1.commissioner_start()
node1.wait(3)

node1.commissioner_add_joiner("TEST123",eui64='*')

# Start node2
node2.ifconfig_up()
node2.set_router_selection_jitter(1)

# Start Joiner on node2 to join the network
node2.joiner_start("TEST123")
node2.wait(10, expect_line="Join success")

# Wait for node 2 to become Router
node2.thread_start()
node2.wait(5)
assert node2.get_state() == "router"
```
rlime 0.1.1
Implementation of R-LIME, a novel method for interpreting the behavior of ML models.
Requires Python: >=3.8

# R-LIME

R-LIME is a novel method that explains behavior of black-box classifiers such as deep neural networks or ensemble models.
It linearly approximizes a decision boundary of the black-box classifier in a local rectangular region, and maximizes the region as long as the approximation accuracy is higher than a given threshold.
Then, it provides contribution of each feature to the prediction and rule that restricted the approximation region.

## Installation
Install the package from this repo:
```sh
pip install rlime
```
## Usage
Example code:
```py
from rlime.rlime import HyperParam, explain_instance
from rlime.utils import load_dataset, get_trg_sample
from sklearn.ensemble import RandomForestClassifier

# Load dataset and sample a focal point
dataset = load_dataset("recidivism", balance=True)
focal_point, _, _ = get_trg_sample(0, dataset)

# Train black-box model (random forest)
black_box = RandomForestClassifier(n_jobs=-1)
black_box.fit(dataset.train, dataset.labels_train)

# Generate explanation
hyper_param = HyperParam()
hyper_param.tau=0.70
rlime_exp = explain_instance(focal_point, dataset, black_box.predict, hyper_param)

# Print generated explanation if found
if rlime_exp is None:
  print("No explanation found")
else:
  rule, arm = rlime_exp
  print(f"Rule: {rule}")
  print("Weights:")
  weights = arm.surrogate_model["LogisticRegression"].weights.values()
  sum_weights = sum([abs(w) for w in weights])
  weights = [w / sum_weights for w in weights]
  top_5 = sorted(enumerate(weights), key=lambda x: abs(x[1]), reverse=True)[:5]
  for i, w in top_5:
    print(f"  {dataset.feature_names[i]:^16}: {w:+.4f}")
  print(f"Accuracy: {arm.n_rewards / arm.n_samples:.4f}")
  print(f"Coverage: {arm.coverage:.4f}")
```
You will get output:
```
Rule: ['Priors = 1']
Weights:
  PrisonViolations: +0.1789
        Age       : -0.1526
    MonthsServed  : +0.1267
        Race      : -0.1191
      Married     : -0.1010
Accuracy: 0.7054
Coverage: 0.4396
```

## Licence
[MIT](https://github.com/g-ohara/rlime/blob/main/LICENSE)

## Author
[Genji Ohara](https://github.com/g-ohara)
itadvisor-client 0.0.21
A python client for the Schneider Electric application IT Advisor.
Requires Python: >=3.8
Keywords: struxureware, ecostruxure, schneider-electric, apc, dco, ita, itadvisor, data center operation

# ITAdvisor Client
![image](https://img.shields.io/badge/pypi-3775A9?style=for-the-badge&logo=pypi&logoColor=white)<br>
An unoffical ITAdvisor API Client.<br> 
<br>
All capabilities available in the API are supported by this module.<br>
<br>
<u><b>Supported IT Advisor Versions:</b></u><br>
- 9.4.4

## Installation
```
pip install itadvisor-client
```

## Documentation
Example:
```
from itadvisor_client import ITAdvisor

# Mr. Robot Inspired Connection Details
ita_client = ITAdvisor(
    host = "itadvisor.evilcorp.com"
    username = "ealderson"
    password = "fS0c13tY"
)

my_genome = ita_client.genomes.get_genomes_by_id(genome_id="9ecca877-7b9f-45e8-ac69-929b5ff87b7e")

```
## Classes and Functions
**assets**
- in-progress

**audit_trail**
- in-progress

**authentication**
- in-progress

**autentication_servers**
- in-progress

**backup**
- list_backup_files()
- download_backup_file(file_name:str, download_directory:str)
- upload_backup_file(backup_file:str)
- get_backup_settings()
- update_backup_settings(backup_retention_days:int, backup_location:str, fail_if_not_mounted:bool, backup_cleanup_enabled:bool)
- trigger_restore(backup_file_name:str)
- trigger_backup(backup_prefix:str="custom_")

**certificates**
- get_certificates()
- add_certificate(certificate:str)
- delete_certificate(certificate:str)

**change_request**
- in-progress

**change_request_template**
- in-progress

**configuration**
- in-progress

**custom_properties**
- get_definitions()
- get_templates()
- check_usage(cp_name:str, cp_value:str)
- get_item_cp(item_id:str)

**customers**
- in-progress

**customers_count**
- get_customers_count(root_location_id, only_active:bool=None, only_with_users:bool=None)

**equipment_browser**
- in-progress

**etl_configuration**
- in-progress

**genomes**
- get_genomes(query:str=None, query_types:list=[], genomes:list=[], genome_source:str=None)
- get_genome_by_id(genome_id:str, genome_library:str=None)

**kpis**
- in-progress

**licenses**
- get_licenses()
- add_license(license_key:str)
- delete_license(license_key:str)

**mail**
- in-progress

**platform_status**
- get_job_queue()
- get_job_status()

**power_capacity**
- in-progress

**power_path**
- in-progress

**routing**
- in-progress

**sensor_mapping**
- in-progress

**struxure_on**
- in-progress

**svg**
- Schneider Electric states DO NOT USE.

**user_groups**
- in-progress

**user_message**
- in-progress

**users**
- in-progress

**work_orders**
- in-progress