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import itertools
from random import random, choice
from math import sqrt
from PIL import Image
import numpy as np
# TODO fix before publicaiton: run analysis seems to have a bug for lavender?! Square lengths are non-constant!? One bug identified, either fix or exclude lavender from the story—leaning towards exclusion, because the absence of zeros spoils the "burst" narrative? But solve the bug first; bugs are always bad even when they "don't matter"
# have to substitute where I already used LAVENDER as a label in story
# also double-check possible label lengths 10/13/16 &c.
WIDTH = LENGTH = 473
# https://stackoverflow.com/a/14382692
def cartesian_to_barycentric(q, p0, p1, p2):
d = 0.5 * (
-p1[1] * p2[0]
+ p0[1] * (-p1[0] + p2[0])
+ p0[0] * (p1[1] - p2[1])
+ p1[0] * p2[1]
)
s = (
1
/ (2 * d)
* (
p0[1] * p2[0]
- p0[0] * p2[1]
+ (p2[1] - p0[1]) * q[0]
+ (p0[0] - p2[0]) * q[1]
)
)
t = (
1
/ (2 * d)
* (
p0[0] * p1[1]
- p0[1] * p1[0]
+ (p0[1] - p1[1]) * q[0]
+ (p1[0] - p0[0]) * q[1]
)
)
return s, t
color_map = {
"RED": (200, 0, 0),
"GREEN": (0, 200, 0),
"BLUE": (0, 0, 200),
"TEAL": (0, 150, 150),
"YELLOW": (240, 240, 0),
# "LAVENDER": (150, 100, 200),
}
def read_label(label):
return ''.join(chr(c) for c in label)
def triangle_points():
return [(157*random(), 157*random()), (315+157*random(), 157+157*random()), (157*random(), 315+157*random())]
def in_triangle(q, points):
s, t = cartesian_to_barycentric(q, *points)
return s > 0 and t > 0 and 1 - s - t > 0
def noise(color):
return [c + 10*random() for c in color]
def triangle_pixels(color):
points = triangle_points()
return [[noise(color) if in_triangle((x, y), points) else noise((0, 0, 0)) for x in range(WIDTH)] for y in range(LENGTH)]
def symmetric_params():
return [(150 + 100*random())/2, 236 + 100*(random() - 0.5), 236 + 100*(random() - 0.5)]
def square_pixels(color):
halflength, x_center, y_center = symmetric_params()
pixels = []
for x in range(WIDTH):
row = []
for y in range(LENGTH):
if abs(x_center - x) < halflength and abs(y_center - y) < halflength:
row.append(noise(color))
else:
row.append(noise((0, 0, 0)))
pixels.append(row)
return pixels
def circle_pixels(color):
radius, x_center, y_center = symmetric_params()
pixels = []
for x in range(WIDTH):
row = []
for y in range(LENGTH):
if sqrt((x - x_center)**2 + (y - y_center)**2) < radius:
row.append(noise(color))
else:
row.append(noise((0, 0, 0)))
pixels.append(row)
return pixels
def data_array(pixels):
data = []
for row in pixels:
for pixel in row:
for channel in pixel:
data.append(int(channel))
return data
def count_burst_lengths(data):
bursts = []
counter = 0
previous = None
for datum in data:
if datum >= 240:
counter += 1
else:
# consecutive "ordinary" numbers mean the burst is over
if counter and previous and previous < 240:
bursts.append(counter)
counter = 0
previous = datum
return bursts
def high_runs(data):
runs = []
high_counter = 0
low_counter = 0
for datum in data:
if datum > 100:
high_counter += 1
low_counter = 0
if datum < 100:
low_counter += 1
if low_counter > 3 and high_counter > 1:
runs.append(high_counter)
low_counter = high_counter = 0
return runs
def run_derivative(run_sequence):
diffs = []
previous = run_sequence[0]
for run in run_sequence[1:]:
diffs.append(run - previous)
previous = run
return diffs
import collections
from itertools import chain, repeat
import operator
def convolve(signal, kernel):
# See: https://betterexplained.com/articles/intuitive-convolution/
# convolve(data, [0.25, 0.25, 0.25, 0.25]) --> Moving average (blur)
# convolve(data, [1, -1]) --> 1st finite difference (1st derivative)
# convolve(data, [1, -2, 1]) --> 2nd finite difference (2nd derivative)
kernel = tuple(kernel)[::-1]
n = len(kernel)
window = collections.deque([0], maxlen=n) * n
for x in chain(signal, repeat(0, n-1)):
window.append(x)
yield round(sum(map(operator.mul, kernel, window)), 2)
def smoothed(data):
smoothed = []
return list(convolve(data, [0.25, 0.25, 0.25, 0.25]))
def find_burst(seq):
for i, el in enumerate(seq):
if el > 100:
return i
def do_shape(color_name, shape_name):
color = color_map[color_name]
if shape_name == "TRIANGLE":
pixels = triangle_pixels(color)
elif shape_name == "SQUARE":
pixels = square_pixels(color)
elif shape_name == "CIRCLE":
pixels = circle_pixels(color)
label = [ord(c) for c in "{} {}".format(color_name, shape_name)]
data = data_array(pixels)
run_analysis = high_runs(data)
diffs = run_derivative(run_analysis)
array = np.array(pixels, dtype=np.uint8)
shape_image = Image.fromarray(array)
shape_image.save("{}_{}.png".format(color_name.lower(), shape_name.lower()))
return data, run_analysis, diffs, label
def random_shape_spec(no_square=False):
if no_square:
shape_names = ["TRIANGLE", "CIRCLE"]
else:
shape_names = ["TRIANGLE", "SQUARE", "CIRCLE"]
color_name = choice(list(color_map.keys()))
shape_name = choice(shape_names)
return color_name, shape_name
def do_random_shape(no_square=False):
color_name, shape_name = random_shape_spec(no_square=no_square)
return do_shape(color_name, shape_name)
def do_answer_key_table_entry(color_name, shape_name):
data, run_analysis, diffs, label = do_shape(color_name, shape_name)
i = find_burst(data) + 1
if shape_name in ["TRIANGLE", "CIRCLE"]:
lengths = "increasing, then decreasing"
else:
lengths = "constant"
if color_name in ["GREEN", "BLUE"]:
pattern_essence = "RED"
else:
pattern_essence = color_name
pattern = ', '.join(str(c) for c in color_map[pattern_essence])
if pattern == (0, 150, 150):
pattern = (150, 150, 0)
print_label = ', '.join(str(c) for c in label)
return "<tr><td>{lengths}</td><td>{pattern}</td><td>{i}</td><td>{print_label}</td></tr>".format(**locals())
def do_random_answer_key_table_entry():
color_name, shape_name = random_shape_spec()
return do_answer_key_table_entry(color_name, shape_name)
p = [('GREEN', 'CIRCLE'),
('BLUE', 'SQUARE'),
('YELLOW', 'CIRCLE'),
('TEAL', 'SQUARE'),
('RED', 'SQUARE'),
('YELLOW', 'SQUARE'),
('BLUE', 'CIRCLE'),
('RED', 'TRIANGLE'),
('BLUE', 'TRIANGLE'),
('GREEN', 'TRIANGLE'),
('YELLOW', 'TRIANGLE'),
('TEAL', 'TRIANGLE'),
('GREEN', 'SQUARE'),
('RED', 'CIRCLE'),
('TEAL', 'CIRCLE')]
def confirm_index(color_name, shape_name):
data, run_analysis, diffs, label = do_shape(color_name, shape_name)
i = find_burst(data) + 1
return (color_name, i%3)
# In [13]: color_index = [confirm_index(c, s) for c, s in p]
# In [15]: sorted(color_index)
# Out[15]:
# [('BLUE', 0),
# ('BLUE', 0),
# ('BLUE', 0),
# ('GREEN', 2),
# ('GREEN', 2),
# ('GREEN', 2),
# ('RED', 1),
# ('RED', 1),
# ('RED', 1),
# ('TEAL', 2),
# ('TEAL', 2),
# ('TEAL', 2),
# ('YELLOW', 1),
# ('YELLOW', 1),
# ('YELLOW', 1)]