semvec/pypi-packages huggingface dataset:
calcifer-pde 0.1.1
Python Poisson solver on curvilinear 2-D grid
Keywords: PDE,temperature,solver,poisson,curvilinear grid

# Calcifer

*Under construction*

Calcifer is a Poisson-like PDE finite difference solver on 2-D structured I.J Grids.
Grids are curvilinear.

* create a **Geometry** object.
* define the computational **Domain**, based upon this **Geometry**.
* Solve the problem. Heat conduction is solver in **HeatSolve** from Calcifer.

## Thermal diffusion, solver by calcifer.


A thermal diffusion test with calcifer is done like this:

```python
from calcifer_pde.domain import Domain
from calcifer_pde.geometry import Square

def heat_solve(dom, k_coeff=1.0):
    init_field = np.random.random_sample(dom.shp1d)
    sterm_l = np.zeros_like(dom.lapl)
    sterm_r = np.zeros(dom.shp1d)
    # Left Hand Side
    lhs_csr = dom.lapl * k_coeff + sterm_l
    # Right-Hand Side
    rhs_csr = np.zeros(dom.shp1d) + sterm_r
    lhs_csr_bc, rhs_csr_bc, grad_n_bc = apply_bc(dom, lhs_csr, rhs_csr)

    out_1d, info = scp.linalg.bicgstab(lhs_csr_bc, rhs_csr_bc, x0=init_field)
    if info == 0:
        print(".   ^_^ Resolution succesfull.")
    elif info > 0:
        print(".   t(-_-t) Resolution failed.")
    else:
        print(".   =_= Convergence not reached.")
    temp = out_1d.reshape(dom.shp2d)
    return temp

geo = Square(nx=80, ny=100, len_x=1.0, len_y=1.0)
dom = Domain(geo)
dom.switch_bc_vmax_neuman(0.0)
dom.switch_bc_vmin_neuman(0.0)
dom.switch_bc_umin_dirichlet(200.0)
dom.switch_bc_umax_dirichlet(100.0)
sol = heat_solve(dom, k_coeff=22.0)

```

One can solve an other PDE by creating a different problem than `heat solve`
wmp 0.2.0
Module for face detection & recognition
Requires Python: >=3.7

# wmp-face

wmp-face is a Python module for face detection & recognition built on 
top of dlib's facial recognizer.

This codebase was developed under Wesleyan Media Project - a research
group that tracks all election-related advertising to give journalists
and political scientists the data and the tools to hold governments and
special interests accountable. Mostly notably, wmp-face was instrumental
in pre-processing the data used for "Do Women Candidates 'Run as Women'
Online?" (Yao, et al.).
insta-hashtag-crawler 0.0.4
A gevent-based simple instagram hashtag crawler
Requires Python: >=3.6

## Getting Started
### Supported Platforms
`insta_hashtag_crawler` has been tested against the following platforms:
* __OS__ : Ubuntu 17.04 LTS, Windows 10
* __Python__ : cPython >= 3.6

### Installation
#### via `pip` (recommended)
* __When python 3 is the only python distribution installed on the system:__
```shell
pip install insta_hashtag_crawler
```
* __With multiple versions of python distributions:__  
  (replace `python3` with the version you want)
```shell
python3 -m pip install insta_hashtag_crawler
```
* __Using `venv`:__  
  + On Linux
```shell
python3 -m venv .venv
source .venv/bin/activate
pip install insta_hashtag_crawler
```
  + On Windows
```cmd
python3 -m venv .venv
cd .venv/Scripts
activate
pip install insta_hashtag_crawler
```
#### via `setup.py`
```shell
git clone https://gitlab.com/point1304/insta_hashtag_crawler.git
cd insta_hashtag_crawler
python3 setup.py bdist
python3 setup.py install
```

### How to
`insta_hashtag_crawler` is shipped with a very simple command line tool  
and this is what you are going to mostly make the use of.

You can start crawling immediately on your `shell` with:
```shell
insta-crawl your-hashtag
```

This command will generates a series of `csv` formatted data in your  
"current working directory".
`csv` file name will be: __${hashtag}_yyyymmdd_(${number}).csv__

## Optional Arguments
* __[--dir] [-d]__  
You can set the directory where crawling results will be generated  
by using [--dir] [-d] option.  
The [--dir] options can take an absolute or relative path as an argument.
```shell
insta-crawl --dir /some/path/to/dir hashtag
insta-crawl --dir some/relative/path hashtag
```

* __[--quiet] [-q]__  
Turn on the [--quiet] [-q] flag if you want to mute the crawling logs,  
which by default are directed to the `stdout`.
This option will redirect any logs to OS-relevant `dev/null`.
realclearpolitics 1.4.0
Python client for RealClearPolitics.
Keywords: politics,polls,rcp,realclearpolitics,web scraping

rcp
===

|Build Status| |codecov|

Python client for RealClearPolitics. 

Install
^^^^^^^

::

    pip install realclearpolitics

Usage
^^^^^

::

    usage: rcp [-h] [--output [OUTPUT]] [--generate-table] url [url ...]

    positional arguments:
      url                The url of the polling data.

    optional arguments:
      -h, --help         show this help message and exit
      --output [OUTPUT]  The output file name.
      --generate-table   Pass this argument to generate a table.



Examples
^^^^^^^^

Get the US general election results.

::

    rcp http://www.realclearpolitics.com/epolls/2016/president/us/general_election_trump_vs_clinton-5491.html --output general.csv

Download multiple polls.

::

    rcp http://www.realclearpolitics.com/epolls/2016/president/us/general_election_trump_vs_clinton-5491.html \
    > https://www.realclearpolitics.com/epolls/other/president_trump_job_approval_economy-6182.html \
    > https://www.realclearpolitics.com/epolls/other/president_trump_job_approval_foreign_policy-6183.html

API Usage
^^^^^^^^^

Search for Fox News poll numbers for Trump:

.. code-block:: python

    from rcp import get_polls, get_poll_data
    from pprint import pprint

    polls = get_polls(candidate="Trump", pollster="Fox")

    for poll in polls:
        td = get_poll_data(poll['url'])
        pprint(td)

The will return a dictionary structured like this:

.. code-block::


    [
        {
            'data': [{'Biden (D)': '49.6',
                    'Date': '3/27 - 7/9',
                    'MoE': '--',
                    'Poll': 'RCP Average',
                    'Sample': '--',
                    'Spread': 'Biden +8.5',
                    'Trump (R)': '41.1'},
            ...
            }],
        'poll': 'https://www.realclearpolitics.com/epolls/2020/president/us/general_election_trump_vs_biden-6247.html'

    ]

Write a poll to CSV:

.. code-block:: python

    from rcp import get_polls, get_poll_data, to_csv

    polls = get_polls(candidate="Biden")[0]
    data = get_poll_data(polls['url'], csv_output=True)
    to_csv('output.csv', data)

Create table:

.. code-block:: python

    from rcp import get_poll_data
    from prettytable import PrettyTable

    x = PrettyTable()

    td = get_poll_data(
        "https://www.realclearpolitics.com/epolls/other/president_trump_job_approval-6179.html"
    )

    x.field_names = list(td[0]["data"][0].keys())
    x.align = "l"

    for row in td[0]["data"]:
        x.add_row(row.values())

    print(x)

.. |Build Status| image:: https://travis-ci.org/AnthonyBloomer/rcp.svg?branch=master
   :target: https://travis-ci.org/AnthonyBloomer/rcp

.. |codecov| image:: https://codecov.io/gh/AnthonyBloomer/rcp/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/AnthonyBloomer/rcp
BGdist2020 0.4
Gaussian distributions
Requires Python: >=3.0

# BGdist2020

This package includes classes and functions for calculating and graphing
both binomial and gaussian distributions.

#Files

Binomialdistribution.py 
Gaussiandistribution.py
Generaldistribution.py

#Installation

The package can be installed using the following command

pip install BGdist2020

#Dependencies

Matplotlib/n
Python 3