MatthewJA

import numpy
import scipy.ndimage.interpolation

def radon_transform(ims):
    """Radon transform a batch of images.
    
    Parameters
    ----------
    ims : numpy.ndarray
      (N x W x H) NumPy array of N images.

MatthewJA

import matplotlib

# http://bkanuka.com/articles/native-latex-plots/
def figsize(scale):
    fig_width_pt = 240.0
    inches_per_pt = 1.0/72.27
    golden_mean = (numpy.sqrt(5.0)-1.0)/2.0
    fig_width = fig_width_pt*inches_per_pt*scale
    fig_height = fig_width*golden_mean
    fig_size = [fig_width,fig_height]

MatthewJA

import io
import warnings

import requests

def get_first(coord: 'SkyCoord', size: 'arcmin'=5) -> 'FITS image':
    data = {
        'RA': coord.to_string(style='hmsdms', sep=' '),
        'Dec': '',
        'Equinox': 'J2000',

MatthewJA

import matplotlib.cm
import matplotlib.colors

cyclic_viridis = matplotlib.colors.LinearSegmentedColormap.from_list(
    'cyclic_viridis',
    [(0, matplotlib.cm.viridis.colors[0]),
     (0.25, matplotlib.cm.viridis.colors[256 // 3]),
     (0.5, matplotlib.cm.viridis.colors[2 * 256 // 3]),
     (0.75, matplotlib.cm.viridis.colors[-1]),
     (1.0, matplotlib.cm.viridis.colors[0])])

MatthewJA

# List of South Australian baby names, 1944-2013.
# Derived from:
#   Attorney-General's Dept, SA,
#   Most popular Baby Names (1944-2013),
#   viewed 24/01/17,
#   https://data.sa.gov.au/data/dataset/popular-baby-names/resource/534d13f2-237c-4448-a6a3-93c07b1bb614
# License: CC BY 4.0
A
A'AFF
A'ARON

MatthewJA

\suppressfontnotfounderror1
\def\myfont{Palatino} % Or whatever your primary font choice is.
\def\myfallback{Palatino Linotype} % Or whatever your fallback font is.
\count255=\interactionmode
\batchmode
\font\foo="\myfont"\space at 10pt
\ifx\foo\nullfont
  \font\foo = "\myfallback"\space at 10pt
  \ifx\foo\nullfont
    \errorstopmode

MatthewJA

Love Hansard but wish it was somehow represented on a map? So do we. That's why we created Question Time.

We love political banter, and we wanted to visualise how political debate occurs over time. For each day of the 2015 -- 2016 NSW Parliament, Question Time visualises mentions of NSW suburbs found in the NSW Parliament Hansard with a heatmap of how unusually often each suburb was mentioned.

We also analyse the top five location mentions in NSW on each sitting day and plot them on the same map. Clicking one of these locations opens data in the sidebar about why it was interesting on that day. We're going to start out with snippets of mentions in Hansard, but plan to include more interesting record information via NLP.

We made extensive use of Python's Flask and also the OpenLayers 3 mapping library that gives us heatmaps overlaid onto regular maps for free.

MatthewJA

data Colour = Red | Green | Blue | Yellow
data Shape = Skinny | Round | Triangular
data Fruit = Apple | Banana | Strawberry

data Appearance = Appearance { colour :: Colour, shape :: Shape }

defruitify :: Fruit -> Appearance
defruitify fruit = case fruit of
  Apple -> Appearance { colour = Red, shape = Round }
  Banana -> Appearance { colour = Yellow, shape = Skinny }

MatthewJA

#!/usr/bin/env python

from subprocess import call
from os import devnull

values = set([

# ("par1", "par2", "par3", "par4"),
# ...,

MatthewJA

import sys
 
import pygame
from pygame.locals import *
 
pygame.init()
 
fps = 60
fpsClock = pygame.time.Clock()