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performance_test.py
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"""
通过封装函数进行性能测试
Created on Thu Aug 6, 2020
"""
from tools.geo_assistant import GeometryAssistant
from random import random, choice
from json import loads
from pandas import read_csv
from time import time
import matplotlib.pyplot as plt
class PD_test(object):
def __init__(self, index):
self.TEST_NUM = 100000
self.compute_bias = 1e-7
self.initialProblem(index)
def main(self, precision):
'''
precision: [grid_precision, digital_precision]
'''
self.precision = precision
self.threshold = precision[0]*0.75
self.initialRecord()
self.stat = [0, 0, 0, 0, 0, 0, 0]
start = time()
for i in range(self.TEST_NUM):
i,j = choice(self.polys_type), choice(self.polys_type)
oi,oj = choice(self.orientation), choice(self.orientation)
row = self.computeRow(i,j,oi,oj)
bounds = self.nfps_bounds[row]
pt = [bounds[0]+(bounds[2]-bounds[0])*random(), bounds[1]+(bounds[3]-bounds[1])*random()]
self.getPolyPtPD(pt, row)
end = time()
print('精确度:{}, 运行时间: {}'.format(self.precision, end-start))
print('存在grid, 使用grid, 存在digital, 存在exterior, 算出不包含, 总计算, 冗余计算: {}'.format(self.stat))
# file = open('record/pd_test.csv', 'a')
# res = str(self.precision[0])+', '+str(end-start)+', '+str(self.stat).replace('[', '').replace(']', '')
# file.write(res+'\n')
# file.close()
return end-start
def getPolyPtPD(self, pt, row):
'''Step 1 首先处理参考点和全部（已经判断了是否包含Bounds）'''
grid_precision, digital_precision = self.precision[0], self.precision[1]
nfp, nfp_parts, convex_status = self.all_nfps[row], self.nfp_parts[row], self.nfps_convex_status[row]
relative_pt = [pt[0] - nfp[0][0], pt[1] - nfp[0][1]]
grid_pt, grid_key = self.getAdjustPt(relative_pt, grid_precision, 5)
digital_pt, digital_key = self.getAdjustPt(relative_pt, digital_precision, 6)
original_grid_pt, original_digital_pt = [grid_pt[0]+nfp[0][0], grid_pt[1]+nfp[0][1]], [digital_pt[0]+nfp[0][0], digital_pt[1]+nfp[0][1]]
'''Step 2 判断是否存在于last_grid_pds和last_digital_pds'''
if grid_key in self.last_grid_pds[row]:
self.stat[0] = self.stat[0]+1
possible_pd = self.last_grid_pds[row][grid_key]
if possible_pd >= self.threshold: # 如果比较大则直接取该值
self.stat[1] = self.stat[1]+1
return possible_pd
if digital_key in self.last_digital_pds[row]: # 如果存在具体的位置
self.stat[2] = self.stat[2]+1
return self.last_digital_pds[row][digital_key]
'''Step 3 判断是否在外部和内外部情况'''
if digital_key in self.last_exterior_pts[row]:
self.stat[3] = self.stat[3]+1
return 0
if len(nfp_parts) > 0:
if not GeometryAssistant.judgeContain(relative_pt,nfp_parts):
self.last_exterior_pts[row][digital_key] = 1
self.stat[4] = self.stat[4]+1
return 0
else:
print('nfp_parts error')
'''Step 4 求解PD结果（存在冗余计算）'''
# grid_pd = GeometryAssistant.getPtNFPPD(original_grid_pt, convex_status, nfp, self.bias)
# self.last_grid_pds[row][grid_key] = grid_pd
# self.stat[5] = self.stat[5]+1
# if grid_pd < self.threshold:
# if abs(digital_pt[0]-grid_pt[0])<self.compute_bias and abs(digital_pt[1]-grid_pt[1])<self.compute_bias:
# digital_pd = grid_pd
# else:
# self.stat[6] = self.stat[6]+1
# digital_pd = GeometryAssistant.getPtNFPPD(original_digital_pt, convex_status, nfp, self.bias)
# self.last_digital_pds[row][digital_key] = digital_pd
# return digital_pd
self.stat[5] = self.stat[5]+1
digital_pd = GeometryAssistant.getPtNFPPD(original_digital_pt, convex_status, nfp, self.bias)
self.last_grid_pds[row][grid_key] = digital_pd
self.last_digital_pds[row][digital_key] = digital_pd
return digital_pd
# return grid_pd
def getAdjustPt(self, pt, precision, zfill_num):
new_pt = [int(pt[0]/precision+0.5)*precision, int(pt[1]/precision+0.5)*precision]
target_key = str(int(new_pt[0]/precision)).zfill(zfill_num) + str(int(new_pt[1]/precision)).zfill(zfill_num)
return new_pt, target_key
def computeRow(self, i, j, oi, oj):
return self.polys_type[j]*self.types_num*len(self.allowed_rotation)*len(self.allowed_rotation) + self.polys_type[i]*len(self.allowed_rotation)*len(self.allowed_rotation) + oj*len(self.allowed_rotation) + oi # i为移动形状，j为固定位置
def initialProblem(self, index):
'''获得某个解，基于该解进行优化'''
_input = read_csv("record/lp_initial.csv")
self.set_name = _input["set_name"][index]
self.width = _input["width"][index]
self.bias = _input["bias"][index]
self.max_overlap = _input["max_overlap"][index]
self.allowed_rotation = loads(_input["allowed_rotation"][index])
self.total_area = _input["total_area"][index]
self.polys, self.best_polys = loads(_input["polys"][index]), loads(_input["polys"][index]) # 获得形状
self.polys_type = loads(_input["polys_type"][index]) # 记录全部形状的种类
self.polys_num = len(self.polys)
self.types_num = _input["types_num"][index] # 记录全部形状的种类
self.orientation, self.best_orientation = loads(_input["orientation"][index]),loads(_input["orientation"][index]) # 当前的形状状态（主要是角度）
self.getPreData() # 获得NFP和全部形状
print("一共",self.polys_num,"个形状")
def initialRecord(self):
self.last_grid_pds = [{} for row in range(len(self.all_nfps))]
self.last_digital_pds = [{} for row in range(len(self.all_nfps))]
self.last_exterior_pts = [{} for row in range(len(self.all_nfps))]
def getPreData(self):
'''获得全部的NFP和各个方向的形状'''
self.all_polygons = [] # 存储所有的形状及其方向
fu = read_csv("data/" + self.set_name + "_orientation.csv")
# 加载全部形状
for i in range(fu.shape[0]):
polygons=[]
for j in self.allowed_rotation:
polygons.append(loads(fu["o_"+str(j)][i]))
self.all_polygons.append(polygons)
# 加载是否对称
if len(self.allowed_rotation) >= 2:
self.vertical_symmetrical = []
for i in range(fu.shape[0]):
self.vertical_symmetrical.append(fu["ver_sym"][i])
if len(self.allowed_rotation) == 4:
self.horizon_symmetrical = []
for i in range(fu.shape[0]):
self.horizon_symmetrical.append(fu["hori_sym"][i])
# 加载NFP的属性
self.nfps_convex_status,self.nfps_vertical_direction,self.nfps_bounds,self.all_nfps,self.nfp_parts = [],[],[],[],[]
nfps = read_csv("data/" + self.set_name + "_nfp.csv") # 获得NFP
for i in range(nfps.shape[0]):
self.nfps_convex_status.append(loads(nfps["convex_status"][i]))
self.nfps_vertical_direction.append(loads(nfps["vertical_direction"][i]))
self.nfps_bounds.append(loads(nfps["bounds"][i]))
self.all_nfps.append(loads(nfps["nfp"][i]))
self.nfp_parts.append(loads(nfps["nfp_parts"][i]))
t=[]
pdt=PD_test(69)
for p in range(1,200):
t.append(pdt.main([p,1]))
plt.plot(range(1,200),t)
plt.show()