semvec/pypi-packages huggingface dataset:
KnoemaUpload 0.4.3
Used for only internal purpose of the knoema employees to upload files to portal via API.
Requires Python: >=3.6

This package is used for only internal purpose of the knoema employees to upload documents to the portal via API.

##Sample Script:##

from Upload import KnoemaUpload 

filepath=r"Path_Of_Your_Document_Upload" # Fill your own Path and Cookies <br />

cookies= {Your_Cookies} # Generate your own cookies. <br />
obj=KnoemaUpload.KnoemaDocumentUploader(filepath,cookies) <br />
obj.FileUpload('client_name') # There is an option to pass 'headers' as parameter in FileUpload() <br />
spotify-playlist-generator-1 0.0.3
A simple to use spotify playlist generator
Requires Python: >=3.6

## Usage

This is a simple spotify playlist generator

```python
from spg import spotify_playlist_generator

spotify = spotify_playlist_generator.SpotifyPlaylistGenerator()
songs_list = spotify.get_songs()
spotify.create_playlist()
```

This simple spotify playlist generator generates a playlist on your spotify account after being provided a series of validation tokens and keys from your spotify account, a date and a range of songs you want on your playlist.

The top 1-100 billboard chart songs are then generated and populated on your brand new spotify playlist

### Installation

Install from PyPi using pip, a package manager for Python.

```
$ python -m pip install spotify-playlist-generator-1
```
jax-codex 0.0.1
COders and DEcoders for jaX.
Requires Python: >=3.7
Keywords: compression,data-compression,machine-learning,deep-learning

# CoDeX

CoDeX contains learned data compression tools for JAX.

You can use this library to build your own ML models with end-to-end optimized
data compression built in. It's useful to find storage-efficient representations
of your data (images, features, examples, etc.) while only sacrificing a small
fraction of model performance.

For a more in-depth introduction from a classical data compression perspective,
consider our [paper on nonlinear transform
coding](https://arxiv.org/abs/2007.03034), or watch @jonycgn's [talk on learned
image compression](https://www.youtube.com/watch?v=x_q7cZviXkY). For an
introduction to lossy data compression from a machine learning perspective, take
a look at @yiboyang's [review paper](https://arxiv.org/abs/2202.06533).

## Documentation & getting help

Please post all questions or comments on
[Discussions](https://github.com/google/codex/discussions). Only file
[Issues](https://github.com/google/codex/issues) for actual bugs or feature
requests. On Discussions, you may get a faster answer, and you help other people
find the question or answer more easily later.

## Installation

To install CoDeX via `pip`, run the following command:

```bash
pip install jax-codex
```

To test that the installation works correctly, you can run the unit tests with:

```bash
python -m codex.all_tests
```

Once the command finishes, you should see a message ```13 passed in 2.76s``` or
similar in the last line.

## Usage

We recommend importing the library from your Python code as follows:

```python
import codex as cdx
```

## Citation

If you use this library for research purposes, please cite:
```
@software{codex_github,
  author = "Ballé, Johannes and Hwang, Sung Jin and Agustsson, Eirikur",
  title = "{CoDeX}: Learned Data Compression in {JAX}",
  url = "http://github.com/google/codex",
  version = "0.0.1",
  year = "2022",
}
```
In the above BibTeX entry, names are top contributors sorted by number of
commits. Please adjust version number and year according to the version that was
actually used.

Note that this is not an officially supported Google product.
pyadvantech 0.2.2
A custom USB driver for Advantech USB-4718

# pyadvantech
## Interact with the Advantech USB-4718
1. Install the module via:
    ```cmd
    pip install pyadvantech
    ```
2. You might need to add the libusb.dll to your Path. 

3. Plug in the USB-4718 into your machine and install a WINUSB driver as its driver.
    ```python
    import pyadvantech.pyadvantech as pya
    
    device = pya.setup(vendor_id=None,device_id=None)
    pya.prepare_read()
    print(pya.read(device))
    ```
    The vendor id and device id can be manually set but if left unspecified will default to the known tested values.    
    Prepare read tells the device that it should now read the thermocouple. This call might not be necessary but it is a good Idea to call it as it moves the device out of a setup stage into the read stage.

4. Changes to the Value Ranges of the device can be made with:
    ```python
    import pyadvantech.pyadvantech as pya
    
    device = pya.setup()
    pya.set_channel_range(device,values=[0x0e,0x0b,0x0b,0x0b,0x0b,0x0b,0x0b,0x0b])
    
    ```
    If values is left as none it will reset the device to the 0-100mv range.
5. Multiple values can be selected and saved with:
   ```python
   import pyadvantech.pyadvantech as pya
   device = pya.setup()
   pya.read_and_save(device,channel=1,length=1000,update=0.1,file_name="test.csv")
   ```
   | Variable Name  | Description | Default Value |
   | ------------- | ------------- | ------------- |
   | device  | The Device handle  | required  |
   | channel  | The channel to be saved and stored  | required [0:7]  |
   | length  | Number of measurements to be taken  | 1000  |
   | update | The update time in sec  | 0.1  |
   | file_name  | The file name must end in .csv  | format "%Y_%m_%d_%H_%M_%S".csv  |
6. An Experimental plotting tool is also provided:
   ```python
   import pyadvantech.pyadvantech as pya
   device = pya.setup()
   pya.plot_out(device,channel=1,x_name=None,y_name=None,x_range=60,update=None,acquisition_len=None)
   ```
   | Variable Name  | Description | Default Value |
   | ------------- | ------------- | ------------- |
   | device  | The Device handle  | required  |
   | channel  | The channel to be saved and stored  | required [0:7]  |
   | x_name  | X axis label  | Time in *update* sec intervals  |
   | y_name  | Y axis label  | Output of USB device  |
   | x_range  | Range of x axis, if left *None* will just add new values continuously  | None  |
   | update | The update time in sec  | 0.1  |
   | acquisition_len  | Number of measurements to be taken  | 500 can be set to "inf"  |