brianckeegan

Note

Going to move development to this repo to make PRs easier to manage: https://github.com/brianckeegan/Post_2024_News

Post-2024 news

Mainstream media failed to hold Trump to account over the course of the 2024 presidential campaign through "view from nowhere" centrism and false equivalencies between both campaigns.

Here is a list of news organizations, newsletters, and writers that are audience-funded and/or non-profits committed to independent and investigative journalism.

Rules

• No centrist contrarians or right-wing authors

brianckeegan

\begin{figure*}[!tb]
\centering
\footnotesize
    \begin{tabular}{cccc}
    
   % First row of triad census
   \begin{tikzpicture}
    	[user/.style={circle,draw=black,fill=black!60,thick,text=white,inner sep=0pt,minimum size=.5cm},
    	->,-stealth,line width = 2, black,
    	font=\boldmath]

brianckeegan

from bs4 import BeautifulSoup
import requests

# Get the data
raw = requests.get('http://www.baseball-reference.com/leagues/MLB/2016-standings.shtml').text

# Parse it
soup = BeautifulSoup(raw,'html.parser')

# Find the table

brianckeegan

Verifying that +bkeegan is my openname (Bitcoin username). https://onename.com/bkeegan

brianckeegan

import numpy as np
import pandas as pd
from bs4 import BeautifulSoup, element
import urllib2, re

# Read the HTML from the webpage on Wikipedia stats and convert to soup
soup = BeautifulSoup(urllib2.urlopen('http://stats.wikimedia.org/EN/TablesWikipediaEN.htm').read())

# Look for all the paragraphs with 2014
_p = soup.findAll('b',text=re.compile('2014'))

brianckeegan

# adapted from http://matplotlib.org/examples/api/barchart_demo.html
def autolabel(rects):
    max_height = max([rect.get_height() for rect in rects if hasattr(rect,'get_height') and not np.isnan(rect.get_height())])
    for rect in rects:
        if hasattr(rect,'get_height'):
            height = rect.get_height()
            if not np.isnan(height):
                ax.text(rect.get_x()+rect.get_width()/2., height-.1*max_height, '%d'%int(height),
                ha='center', va='bottom',color='w')

brianckeegan

import pandas as pd
import numpy as np
import datetime

dft = pd.DataFrame({'A' : ['spam', 'eggs', 'spam', 'eggs'] * 6,
                   'B' : ['alpha', 'beta', 'gamma'] * 8,
                   'C' : [np.random.choice(pd.date_range(datetime.datetime(2013,1,1),datetime.datetime(2013,1,3))) for i in range(24)],
                   'D' : np.random.randn(24),
                   'E' : np.random.random_integers(0,4,24)})

brianckeegan

# Serrano, Boguna, Vespigani backbone extractor
# from http://www.pnas.org/content/106/16/6483.abstract
# Thanks to Michael Conover and Qian Zhang at Indiana with help on earlier versions
# Thanks to Clay Davis for pointing out an error

import networkx as nx
import numpy as np

def extract_backbone(g, weight='weight', alpha=.05):
    backbone_graph = nx.Graph()