semvec/pypi-packages huggingface dataset:
rolling-quantiles 1.1.0
Composable and blazing fast rolling-quantile filters for streaming data and bulk batches.
Requires Python: >=3.7
Keywords: numpy,filter,numeric,signal,streaming,scipy,quantiles,rolling,efficient,realtime

# Rolling Quantiles for NumPy
## Hyper-efficient and composable filters.

* Simple, clean, intuitive interface.
* Supports streaming data or bulk processing.
* Python 3 bindings for a compact library written in pure C.

### A Quick Tour

```python
import numpy as np
import rolling_quantiles as rq

pipe = rq.Pipeline( # rq.Pipeline is the only stateful object
  # declare a cascade of filters by a sequence of immutable description objects
  rq.LowPass(window=200, portion=100, subsample_rate=2),
    # the above takes a median (100 out of 200) of the most recent 200 points
    # and then spits out every other one
  rq.HighPass(window=10, portion=3,  subsample_rate=1))
    # that subsampled rolling median is then fed into this filter that takes a
    # 30% quantile on a window of size 10, and subtracts it from its raw input

# the pipeline exposes a set of read-only attributes that describe it
pipe.lag # = 60.0, the effective number of time units that the real-time output
         #   is delayed from the input
pipe.stride # = 2, how many inputs it takes to produce an output
            #  (>1 due to subsampling)


input = np.random.randn(1000)
output = pipe.feed(input) # the core, singular exposed method

# every other output will be a NaN to demarcate unready values
subsampled_output = output[1::pipe.stride]
```

See the [Github repository](https://github.com/marmarelis/rolling-quantiles) for more details.
TextRandAug 0.0.4
We are highly thankful to Dr. Paul Buitelaar, Dr. Omnia Zayed, Dr. Mihael Arcan, Dr. John McCrae and Janet Choi. This module was built during 5th week CRT-AI training (NLP week) at National University of Ireland, Galway)
Keywords: python,text data augmentation,random augmentation,randAug

Inspired by state-of-the-art data augmentation technique, RandAug, we propose Text random data augmentation, that use randAug approach for text data. Any issue faced by you should be reported to i141637@nu.edu.pk 
 Description of function is parameteres: 
 training original: is input file in txt format, each line have a label separated from sentence by tab 
  output file: output file path 
        alpha_sr: parameter for controlling replacement 
       alpha_ri: parameter for controlling random insertion  
 alpha_rs: parameter for controlling random swaping 
  alpha_rd: parameter for controlling random deletion 
 num_aug: is number of augmentation, by default 9. Examples with demo will soon be released, (Under progress)
no-cap 0.0.0rc0
Write once with inspection of python objects with type hinting from Python's standard library `typing` to generate a ConfigArgParser.
Requires Python: >=3.7

## No CAP: Typing to ConfigArgParse

Write once with inspection of type hinted python objects from Python's standard library [typing](https://docs.python.org/3/library/typing.html) to generate a ConfigArgParser.
[ConfigArgParser](https://github.com/bw2/ConfigArgParse) is a drop in replacement for python's argparse that adds support for parsing configuration files and environment variables in addition to argparse's command line interface.
Type hinting your python objects should be enough to generate a hierarchical ConfigArgParser automtically through inspection.

If you want to generate a ConfigArgParser from your docstrings, see [docstr](https://github.com/prijatelj/docstr), which will depend upon this project.

Considering using `typing_inspect`.
navaly 0.0.1
Downloads and extracts text, html from different formats

## naval

### Description
`naval` downloads, fetches and extract text, html from url, file path,
file object and more. Downloading(fetching) data from url will be handled
for you. `pdf`, `docx`, `pptx`, `html`, and `text` files are supported out of 
box.  

File can be from URL, file path, file object or even bytes. URL can point
to webpage instead of file e.g [google.com](https://google.com/) which
will be treated as html. Its all about file extensions, `file.html` will be
treated as html unless explicitly specified.

### Install
naval can be installed with pip  
```bash 
pip install navaly
```

### Usage
downloading from url:

```python
# download to file in path
naval.download("http://example.com/", "output.html")
naval.download("http://example.com/sample.pdf", "output.pdf")

# download to file like object
file_output = BytesIO()
naval.download("http://example.com/sample.pdf", file_output)

# download from multiple urls into folder
# html file will downloaded to 'downloads/' folder
urls = ["http://example.com/", "https://www.google.com/"]
naval.download_all(urls, "downloads")
```

Extract text and html
```python
# extract text from pdf, docx and pptx files
output_text = naval.extract_text("sample_file.pdf")
output_text = naval.extract_text("sample_file.pptx")
output_html = naval.extract_html("sample_file.docx")
output_text = naval.extract_text("http://example.com/")

# Extract from file like object
with open("sample_file.pdf", mode="rb") as file:
    output_text = naval.extract_text(input_file)

# extract to file(file path, file object)
naval.extract_text_to_file("sample_file.pdf", "output.txt")
naval.extract_html_to_file("sample_file.pdf", "output.html")

# string can passed directly
html = '''
<p> First paragraph </p>
<p> Second paragraph </p>
'''
output_text = naval.extract_text(html, source_locates_data=False, content_type="text/html")

# same with bytes
with open("sample_file.pdf", mode="rb") as file:
    pdf_bytes = file.read()
    output_html = naval.extract_html(pdf_bytes, source_locates_data=False, content_type="application/pdf")

```
> See [examples/](https://github.com/Sekgobela-Kevin/naval/tree/main/examples) folder for more examples

### Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
> Dont forget to update tests.