Implemented!!

This commit is contained in:
JasterV 2020-06-08 05:17:06 +02:00
parent a996cc619b
commit 2c521433d3
4 changed files with 90 additions and 48 deletions

84
src/closest_colors.py Normal file
View file

@ -0,0 +1,84 @@
import requests
import json
from vector_utils import Vector as vec
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
import math
def get_content(url):
response = requests.get(url)
content = response.content.decode()
return content
def get_json(url):
content = get_content(url)
data = json.loads(content)
return data
def hex_to_rgb(s):
s = s.lstrip('#')
return [int(s[:2], 16), int(s[2:4], 16), int(s[4:6], 16)]
def parse_colors(data_set):
return dict((item['color'], hex_to_rgb(item['hex'])) for item in data_set['colors'])
def calculate_size(dist):
pass
def closest(colors, to, n):
closest_colors = ((c, vec.distance(to, c)) for c in colors.values())
return sorted(closest_colors, key=lambda c: c[1])[:n]
def plot_data(ax, values, colors, sizes):
xdata, ydata, zdata = zip(*values)
ax.scatter3D(xdata, ydata, zdata, c=colors /
255.0, s=sizes, depthshade=False)
def plot_closest(ax, colors, to):
distances = closest(colors, to, n=len(colors)//2)
max_distance = max(distances, key=lambda x: x[1])[1]
sizes = np.array(
list(map(lambda x: (math.log2(max_distance) - math.log2(x[1] + 1))*100, distances)))
rgb_values = list(map(lambda x: x[0], distances))
plot_data(ax, rgb_values, np.array(rgb_values), sizes)
def init_projection(size=(13, 10)):
plt.figure(figsize=size, dpi=90)
ax = plt.axes(projection='3d')
ax.set_axis_off()
return ax
def print_colors(colors):
print("\n-----COLOR LIST-----")
for color in colors.keys():
print(color)
if __name__ == '__main__':
data_set_url = "https://raw.githubusercontent.com/dariusk/corpora/master/data/colors/xkcd.json"
data_set = get_json(data_set_url)
colors = parse_colors(data_set)
print_colors(colors)
color = input(
"\nEnter a color from the color list or just enter an hexadecimal value: ")
if color in colors:
color = colors[color]
else:
color = hex_to_rgb(color)
ax = init_projection()
plot_closest(ax, colors, color)
plt.show()

View file

@ -1,43 +0,0 @@
import requests
import json
from vector_utils import Vector as vec
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
def get_content(url):
response = requests.get(url)
content = response.content.decode()
return content
def get_json(url):
content = get_content(url)
data = json.loads(content)
return data
def hex_to_rgb(s):
s = s.lstrip('#')
return [int(s[:2], 16), int(s[2:4], 16), int(s[4:6], 16)]
def parse_colors(data_set):
return dict((item['color'], hex_to_rgb(item['hex'])) for item in data_set['colors'])
def closest(space, coord, n=10):
return sorted(space.keys(), key=lambda x: vec.distance(space[x], coord))[:n]
if __name__ == '__main__':
data_set_url = "https://raw.githubusercontent.com/dariusk/corpora/master/data/colors/xkcd.json"
data_set = get_json(data_set_url)
colors = parse_colors(data_set)
fig = plt.figure()
ax = plt.axes(projection='3d')
plt.show(fig)

View file

@ -1,5 +1,6 @@
import math
from functools import reduce
import operator
class Vector:
@staticmethod
@ -8,15 +9,15 @@ class Vector:
@staticmethod
def add(*vecs):
return [i + j for i, j in zip(*vecs)]
return [sum(t) for t in zip(*vecs)]
@staticmethod
def subtract(*vecs):
return [i - j for i, j in zip(*vecs)]
return [reduce(operator.__sub__, t) for t in zip(*vecs)]
@staticmethod
def mean(*vecs):
return [float(coord) / len(vecs) for coord in reduce(Vector.add, vecs)]
def mean(*vectors):
return [float(coord) / len(vectors) for coord in reduce(Vector.add, vectors)]

View file

@ -2,7 +2,7 @@ import unittest
from src.vector_utils import Vector
class TestStringMethods(unittest.TestCase):
class TestVector(unittest.TestCase):
def test_add(self):
v1 = [2, 3]