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timing and remove unused calc func day 10

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Xevion
2019-11-18 08:45:56 -06:00
parent a5a417e92a
commit a594395cbf
1 changed files with 6 additions and 6 deletions
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12
2018/day-10/python/main.py
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@@ -1,12 +1,11 @@
import re import re
import os import os
import sys import sys
import time
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from collections import namedtuple
# Constants # Constants
@@ -24,9 +23,6 @@ class MovingPoint(object):
self.pos = [int(match.group(1)), int(match.group(2))] self.pos = [int(match.group(1)), int(match.group(2))]
self.vel = [int(match.group(3)), int(match.group(4))] self.vel = [int(match.group(3)), int(match.group(4))]
def calculate(self, t):
return Point(self.pos[0] + (self.vel[0] * t), self.pos[1] + (self.vel[1] * t))
def simulate(self, t=1): def simulate(self, t=1):
self.pos[0] += self.vel[0] * t self.pos[0] += self.vel[0] * t
self.pos[1] += self.vel[1] * t self.pos[1] += self.vel[1] * t
@@ -34,6 +30,8 @@ class MovingPoint(object):
def __repr__(self): def __repr__(self):
return f"<MovingPoint pos({self.pos[0]}, {self.pos[1]}) vel({self.vel[0]}, {self.vel[1]})" return f"<MovingPoint pos({self.pos[0]}, {self.pos[1]}) vel({self.vel[0]}, {self.vel[1]})"
# Matrix class for managing a list of points and quickly running simulation
class Matrix(object): class Matrix(object):
def __init__(self, input): def __init__(self, input):
self.points = [] self.points = []
@@ -79,12 +77,14 @@ print('Matrix built, copy made.')
# Simulate all likely points in time of message # Simulate all likely points in time of message
print('Simulating') print('Simulating')
t1 = time.time()
for i in range(11000): for i in range(11000):
difX = abs(matrix.maxX - matrix.minX) difX = abs(matrix.maxX - matrix.minX)
difY = abs(matrix.maxY - matrix.minY) difY = abs(matrix.maxY - matrix.minY)
difs.append(difX + difY) difs.append(difX + difY)
matrix.simulate(1) matrix.simulate(1)
print('Simulated') t2 = time.time()
print(f'Simulated in {round(t2 - t1, 2)} seconds')
# Point in time where all points are closest # Point in time where all points are closest
best = min(list(enumerate(difs)), key=lambda x:x[1]) best = min(list(enumerate(difs)), key=lambda x:x[1])