sneakers-the-rat

# modeling this ridiculous healthcare proposal

import numpy as np
import matplotlib.pyplot as plt

def healthcare(steps=11, initial_cost=2500000., mean_increase=6., sd_increase=2.):
    # stash initial values
    cost = initial_cost
    uo_cost = cost*0.95
    increase_pct = 0.

sneakers-the-rat

library(googlesheets)
library(tidyverse)

# get spreadsheet key
#sheet_key <- extract_key_from_url("https://docs.google.com/spreadsheets/d/e/2PACX-1vRyiiW8T0JFOnycEhQM6WaoUgcA_5xpO5xe-x0ifhI38XfmOpAuUvbtMCw8Lg6CuX5W--wbh0XFk9KU/pubhtml")
# stash so it dont crash during class
sheet_key <- "1TbkdZ_FSdf-PBqPwNBFcEVCANVgb1xgXv70GhqjmWrk"
# download sheet
emotions_raw <- gs_key(sheet_key) %>% gs_read()

sneakers-the-rat

import requests
from bs4 import BeautifulSoup as bs
from tqdm import tqdm
import json
import os
import tables
import numpy as np
import pdb
import traceback

sneakers-the-rat

[
  "Dear Colleagues, Universities today face headwinds from a multitude of sources. In the political sphere, one party castigates us for being ideologically unbalanced and intolerant of free speech and the other for being unaffordable and elitist. Trust in universities is battered by a deluge of criticism and well-publicized scandals involving sexual violence, administrative misfeasance, and admissions improprieties. Political support has diminished with public trust. Many public universities, including the University of Oregon, never recovered from the 2008 recession in terms of state funding. Underfunded pension plans, skyrocketing health-care costs, and skepticism about our mission and operation have created tremendous pressure on our twin missions of education and research. Despite these challenges, I am incredibly optimistic about the future of higher education, generally, and at the University of Oregon, in particular. I know some of you will read this, roll your eyes, and think to yourself, \u201cHe g

sneakers-the-rat

library(psych)
library(ggplot2)
library(plyr)
library(lme4)
library(lmerTest)

n_sims <- 1000

sims <- expand.grid(
  i_sim     =  seq(n_sims),

sneakers-the-rat

library(rvest)
library(dplyr)
library(stringr)

m200 <- read_html("http://www.alltime-athletics.com/m_100ok.htm")

m2_table <- m200 %>%
  html_nodes("body > center:nth-child(8) > pre")

# make list of rows, split by newline character

sneakers-the-rat

<scheme name="Peacocks In Space Contrast (rainglow)" version="142" parent_scheme="Darcula">
  <option name="FONT_SCALE" value="1.0" />
  <metaInfo>
    <property name="created">2019-01-03T19:28:35</property>
    <property name="ide">PyCharmCore</property>
    <property name="ideVersion">2018.2.0.0</property>
    <property name="modified">2019-01-03T19:28:43</property>
    <property name="originalScheme">_@user_Peacocks In Space Contrast (rainglow)</property>
  </metaInfo>
  <option name="LINE_SPACING" value="1.4" />

sneakers-the-rat

# renamed files to 'vid.avi' and 'points.csv'
# deleted first row of points.csv

# call this and use -p 'y'/'n', -r 'y'/'n' to control playing and rendering the video
# or do ./elliot_pupil.py --help

# you could comment out the skvideo and tqdm imports and objects if ya don't want to install/render.
import cv2
import pandas as pd
import numpy as np

sneakers-the-rat

library(ggplot2)

jobs <- data.frame(start=c(2000, 2006, 2008, 2009, 2014),
                   end  =c(2005, 2009, 2010, 2015, 2015),
                   idx = rev(seq(5)),
                   name =as.ordered(c("job a", 'job b', 'job c', 'job d', 'job e')))

g.job <- ggplot(jobs)+
  geom_rect(aes(xmin=start, xmax=end, ymin=idx, ymax=idx+1, fill=name))+
  scale_fill_grey(start=0, end=0.8)+

sneakers-the-rat

# a more or less line-for-line transcription of the source available here:
# https://docs.google.com/open?id=0B10RxHxW3I92dG9SU0pNMV84alk
# which is described in this paper:
# http://ieeexplore.ieee.org/document/6166585/
# D. K. Prasad, C. Quek, M. K. H. Leung and S. Y. Cho, "A parameter independent line fitting method," The First Asian Conference on Pattern Recognition, Beijing, 2011, pp. 441-445.
# doi: 10.1109/ACPR.2011.6166585
#
#
# Operation is pretty simple, just pass an **ordered** Nx2 array of x/y coordinates, get line segments.
# A convenience ordering function is provided free of charge ;)