semvec/pypi-packages huggingface dataset:
scrab 0.0.6
Fast and easy to use scraper for the content-centered web pages, e.g. blog posts, news, etc.
License: MIT
Requires Python: >=3.8
Keywords: scrab scraper crawler extractor converter web content html text

# scrab - Fuzzy content scraper

[![Python package](https://github.com/gindex/scrab/workflows/Python%20package/badge.svg?branch=master)](https://github.com/gindex/scrab/actions)
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[![GitHub Release](https://img.shields.io/github/v/release/gindex/scrab.svg)](https://github.com/gindex/scrab/releases) 
[![GitHub Release](https://img.shields.io/pypi/v/scrab.svg)](https://pypi.org/project/scrab) 
[![License: MIT](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://opensource.org/licenses/MIT)


Fast and easy to use content scraper for topic-centred web pages, e.g. blog posts, news and wikis.    

The tool uses heuristics to extract main content and ignores surrounding noise. No processing rules. No XPath. No configuration.

### Installing

```shell script
pip install scrab
```

### Usage
```shell script
scrab https://blog.post
``` 

Store extracted content to a file:

```shell script
scrab https://blog.post > content.txt
``` 

### ToDo List
- [x] Support `<main>` tag 
- [ ] Add support for lists
- [ ] Add support for scripts 
- [ ] Add support for markdown output format
- [ ] Download and save referenced images
- [ ] Extract and embed links
 
### Development
```shell script
# Lint with flake8
flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics

# Check with mypy
mypy ./scrab
mypy ./tests

# Run tests
pytest
``` 
Publish to PyPI:
```shell script
rm -rf dist/*
python setup.py sdist bdist_wheel
twine upload dist/*
```
 
### License
This project is licensed under the [MIT License](README.md).
coin-seer 0.1.4
Automatic cryptocurrency forecasting
License: MIT
Requires Python: >=3.7
Keywords: cryptocurrency forecasting

<p align="center">
  <img src="https://raw.githubusercontent.com/hnnhvwht/coin-seer/master/docs/img/coin-seer-logo.png">
</p>

<h2 align="center">Automatic Cryptocurrency Forecasting</h2>

Coin Seer is a Python package that provides a predictive modelling pipeline for cryptocurrencies. It automatically preprocesses time series, computes descriptive statistics, fits models, makes forecasts, generates evaluation metrics, and creates plots. Coin Seer adopts an analyst-in-the-loop approach by providing extensive logging capabilities and emphasizes statistical robustness.

Coin Seer is presently in the alpha stage and more features and tests are being developed actively. Please send bug reports and feature requests to the [author](mailto:hnnh.v.wht@gmail.com) or post them as [issues](https://github.com/hnnhvwht/coin-seer/issues).

## Quick Start

Install Coin Seer, e.g., from PyPI:

```bash
$ python -m pip install coin-seer
```

The `CoinSeer` class provides an API for predictive modelling. Instantiate it using a datetime-indexed time series in `pandas.Series` format (here, `ts`) and a currency pair, e.g.,

```python
>>> from coinseer.api import CoinSeer
>>> cs = CoinSeer(ts, 'btc-eth')
```

By default, Coin Seer preprocesses the time series and converts it to its 1-step log returns. You can control this behaviour by passing in keyword arguments.

Explore the data by calling `cs.dashboard()` and `cs.describe()`. When ready, select a model and prepare forecasts:

```python
>>> cs.select('arima')
>>> cs.run(20)
```

This code will automatically select a best autoregressive integrated moving average (ARIMA) model, fitted to the whole time series. It will then generate 20 point forecasts off the end of the time series. Analyze the results using the following methods:

- `cs.log()`, to output the model selection log;
- `cs.evaluate()`, to display any evaluation metrics;
- `cs.display_results()`, to plot the forecasts along with any diagnostic information; and
- `cs.forecasts()`, to get the forecast values directly.

For more information about the API and the available models, [read the docs](https://coin-seer.readthedocs.io/en/latest/)!

## Planned updates

- Write more tests
- Improve support for prediction intervals and add density forecasting capabilities
- Incorporate GARCH and LSTM models
- Improve plotting (legend placement, themes, tick counts, etc.)
- Add/improve functions for saving and loading models, logs, and plots to disk
- General refactoring, particularly of `models.arima`

## Authors

Coin Seer is written and maintained by [Hannah White](mailto:hnnh.v.wht@gmail.com).

## Acknowledgements

The Coin Seer logo is set in Indestructible Type's [Bodoni*](https://indestructibletype.com/Bodoni.html).
dPCA 1.0.5
Implements Demixed Principal Components Analysis
License: MIT

dPCA is a linear dimensionality reduction technique that automatically discovers and highlights the essential features of complex population activities. The population activity is decomposed into a few demixed components that capture most of the variance in the data and that highlight the dynamic tuning of the population to various task parameters, such as stimuli, decisions, rewards, etc.

.. code-block::

    @article{kobak2016dpca,
       title={Demixed principal component analysis of neural population data},
       volume={5},
       ISSN={2050-084X},
       url={http://dx.doi.org/10.7554/eLife.10989},
       DOI={10.7554/elife.10989},
       journal={eLife},
       publisher={eLife Sciences Publications, Ltd},
       author={Kobak, Dmitry and Brendel, Wieland and Constantinidis, Christos and Feierstein, Claudia E and Kepecs, Adam and Mainen, Zachary F and Qi, Xue-Lian and Romo, Ranulfo and Uchida, Naoshige and Machens, Christian K},
       year={2016},
       month={Apr}
    }

🚀 Quickstart
-------------

.. code-block:: bash

   pip install dpca


🎉 Use dPCA
-----------
Simple example code for surrogate data can be found in [**dpca_demo.ipynb**](http://nbviewer.ipython.org/github/wielandbrendel/dPCA/blob/master/python/dPCA_demo.ipynb) and **dpca_demo.m**.

API of dPCA is similar to sklearn. To use dPCA, you should first import dPCA and initialize it before callling the fitting function,

.. code-block:: python

   from dpca import dPCA
   dpca = dPCA(labels, n_components, regularizer)
   Z = dpca.fit_transform(X)
   
The required initialization parameters are:

* **X:** A multidimensional array containing the trial-averaged data. E.g. X[n,t,s,d] could correspond to the mean response of the *n*-th neuron at time *t* in trials with stimulus *s* and decision *d*. The observable (e.g. neuron index) needs to come first.
* **labels:** Optional; list of characters with which to describe the parameter axes, e.g. 'tsd' to denote time, stimulus and decision axis. All marginalizations (e.g. time-stimulus) are refered to by subsets of those characters (e.g. 'ts').
* **n_components:** Dictionary or integer; if integer use the same number of components in each marginalization, otherwise every (key,value) pair refers to the number of components (value) in a marginalization (key).

More detailed documentation, and additional options, can be found in **dpca.py**.