semvec/pypi-packages huggingface dataset:
keras-data-format-converter 0.1.24
Generates equal keras models with the desired data format
Requires Python: <3.11,>=3.8

# Keras data format converter

Generates equal keras models with the desired data format  


## Requirements
tensorflow >= 2.0


## API
`convert_channels_first_to_last(model: keras.Model, inputs_to_transpose: List[str] = None, verbose: bool = False) -> keras.Model`

`convert_channels_last_to_first(model: tf.keras.Model, inputs_to_transpose: List[str] = None, verbose: bool = False) \
        -> tf.keras.Model`

`model`: Keras model to convert

`inputs_to_transpose`: list of input names that need to be transposed due tothe data foramt changing  

`verbose`: detailed output

## Getting started

```python
from tensorflow import keras
from keras_data_format_converter import convert_channels_last_to_first

# Load Keras model
keras_model = keras.models.load_model("my_image_model")

# Call the converter (image_input is an input that needs to be transposed, can be different for your model)
converted_model = convert_channels_last_to_first(keras_model, ["image_input"])
```

## Supported Layers with Special handling
- [X] Normalization layers
- [x] Permute
- [x] Reshape
- [x] Concatenate
- [ ] Dot
- [ ] MultiHeadAttention
- [ ] TFOpLambda (Inserted by the Functional API construction whenever users call
  a supported TF symbol on KerasTensors, see [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/layers/core.py#L1284) at Tensorflow repo for more info)

## Unsupported Layers due to lack of data_format property
- Cropping1D
- Upsampling1D
- Zeropadding1D
- All layers in tensorflow.keras.preprocessing

## How to deploy
- Create a new release version on GitHub
- Update parameters in setup.py (usually `version` and `download_url`)
- Run `python setup.py sdist` in root directory
- Run `pip install twine`
- Run `twine upload dist/*`
 


## License
This software is covered by MIT License.
ssht 3.0.0
SSH client wrapper for easily connecting to hosts
Requires Python: <3.15,>=3.10

# ssht
SSH client wrapper for easily connecting to hosts

[![Coverage Status](https://coveralls.io/repos/github/hkraal/ssht/badge.svg?branch=master)](https://coveralls.io/github/hkraal/ssht?branch=master)
![](https://img.shields.io/pypi/pyversions/ssht.svg?style=flat)

This wrapper for the well known `ssh` client makes it possible to connect to the right server with just typing a part of the host name. The external sources are queried using the search term and those matching the string will be presented as an option.

Current supported sources:

* JSON file
* MySQL database
* JSON API endpoint
* Custom parser class

### Installation

Install ssht using pip:

    pip3 install ssht

or if you want the latest version:

    pip3 install https://github.com/hkraal/ssht/archive/master.zip


### Usage

    ssht [-h] name [-4] [-6]

    positional arguments:
      name        name of the host to connect to

    optional arguments:
      -h, --help  show this help message and exit
      -4          connect using ipv4 (skip dns if ipv4 address is defined)
      -6          connect using ipv6 (skip dns of ipv6 address is defined)

Example of a connection

    $ ssht host01
    1) root@host01.exmaple.com
    2) host01.exmaple.com
    Connect to: 1
    Connecting to "host01.example.com"
    root@host01:~$

### Configuration

Create ssht folder in home directory

    mkdir ~/.ssht

Configure sources in ~/.ssht:

**Define servers in JSON format**:

    {
    	"hosts": [
    	  {
    		"port": "2222",
    		"hostname": "host01.example.com",
    		"ipv4": "192.168.0.2",
    		"user": "root"
    	  },
    	  {
    		"port": "2222",
    		"hostname": "host01.example.com",
    		"ipv4": "192.168.0.2",
    	  }
    	]
    }

**Define servers in MySQL**:

    {
      "config": {
        "host": "localhost",
        "user": "username",
        "password": "passeword",
        "database": "infra"
      },
      "query": "SELECT hostname, port, ipv4, ipv6, user FROM servers"
    }

**Define JSON API endpoint**:

	{
		"config": {
			"url": "http://ssht-api.dev",
			"headers": {
				"Authentication": "bla:huk"
			}
		}
	}

**Define custom class**:

1) Create a Python package containing your class

```
from ssht.plugins import Parser, Host


class ExampleParser(Parser):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._files = self.get_files('.custom')

    def search(self, needle):
        return [
            Host(hostname='host01.example.com', ipv4='192.168.0.2'),
        ]
```

2) Enable your class in `~/.ssht/config.json`

```
{
    "parsers":
    [
        "ssht.plugins.JsonParser",
        "ssht_provider.ExampleParser"
    ]
}
```
particle-pack-tools 0.0.7
A toolkit for processing and visualizing particle pack data.
Requires Python: >=3.11
Keywords: particle pack, tools, visualization, data processing, particle pack

# Particle Pack Tools

## Installation
To install the package, run:
```
pip install particle-pack-tools
```

## Usage

### Dataset I/O
Load particle pack datasets using:
```
from pptools.io import load_nc, load_nc_arr
```

### Dataset Preprocessing
Preprocess particle pack datasets with:
```
from pptools.preprocess import crop_3d
# ...additional preprocessing functions...
```

### Dataset Visualization
Visualize particle pack datasets using the `Visualizer` class:
```
from pptools.visualize import Visualizer

# Initialize the visualizer
vis = Visualizer()

# Plot a tomogram
vis.plot_tomo(tomo_img)
vis.show()

# Plot a multi-label mask
vis.plot_mask(mask_img)
vis.show()
```

---

Feel free to report any issues or share suggestions to help improve this toolkit.
farsi-number-words 1.0.0
Complete Persian number to words converter with multiple output formats
Requires Python: >=3.7
Keywords: persian, farsi, number, words, converter, currency, text

pip install farsi-number-words

# استفاده در پایتون
from farsi_number import NumberToWords
converter = NumberToWords()
print(converter.convert(1250000, currency='toman'))

# استفاده از CLI
farsi-number 1250000 500000 --currency toman --output json