Ram-N

#' num_bkts should be a non-zero integer
#' col should contain numeric values
equi_bucket <- function(col, num_bkts){
  
  if(!is.numeric(col)){
    warning("Values passed to Equibucket are not numeric")
    return(NA)
  }  
  xmin = min(col, na.rm = TRUE)
  eps = 1/(num_bkts-1)

Ram-N

plot_pax_wrap <- function(df, num_pax){
  pax_rows <- round(sqrt(num_pax), -1)
  pax_cols <- as.integer(num_pax/pax_rows)
  df2 <- arrange(df, -Rev)
  curr_pax <- 1
  for (py in 1:pax_cols){
    for (px in 1:pax_rows){
      if (curr_pax <= num_pax){
        df2[curr_pax, 'plot_col'] = px

Ram-N

import json
from wordcloud import WordCloud
import matplotlib.pyplot as plt


with open('data/trump_tweets/condensed_2018.json') as f:
    data = json.load(f)

type(data), len(data)

Ram-N

w, h = 800, 800


def setup():
    size(w, h)
    stroke(0, 0, 250)  # blue


num_steps = 200
step = TWO_PI / num_steps

Ram-N

w, h = 1000, 800

class Ball(object):
    
    def __init__(self,_id, _x, _y, _vx=0, _vy=0, _dir='UP'):
        self.x, self.y = _x, _y
        self.vx, self.vy = _vx, _vy
        self.id = _id
        self.dir = _dir #'UP', 'DOWN', "RIGHT", "LEFT"
        self.av = 0

Ram-N

# The noise value was calculated at each pixel, 
# using Processing’s built-in noise function. 
# This value was scaled up (by a factor of 10 to 100) and rounded, 
# yielding an integer for every pixel in the frame. 
# Colors were designated based on this number using a switch. 
# Finally, a point/dot was drawn at each pixel, in the assigned color. 
# The greater the scaling factor, the thinner and more numerous the noise bands. 


# Creates a 2D List of 0's, nCols x nRows large

Ram-N

class Ball(object):
    
    def __init__(self,_id, _x, _y, _vx=0, _vy=0):
        self.x, self.y = _x, _y
        self.vx, self.vy = _vx, _vy
        self.id = _id
        #store the starting coords of each ball. For relaunching.
        self.startx, self.starty = _x, _y
        self.active = False
        

Ram-N

radius = 100

def setup():
    size(800, 600)
    background(255)
    smooth()
    noStroke()
    fill(0, 0, 200)
    frameRate(100)
    

Ram-N

import pandas as pd

def print_songs_found(songs_column, clue_words):
    for song in songs_column:
        try:
            songwords = [x.lower() for x in song.split()]
            #print(songwords)
            if all(word in clue_words  for word in songwords):
                print(songwords)
        except:

Ram-N

i <- 1:50
b <- 1:5
ColNames <- paste0("X_", i, "_", rep(b, each=length(i)))