既然学习一段时间python了，那么得拿些好玩的东西练练手，这里通过加载几万局的吃鸡数据，来对吃鸡胜率进行可视化分析。
通过绘制击杀地图和被击杀地图查找LYB的藏身之地
下面贴上代码，和分析。
#这个代码，是通过展示地图击杀和死亡最多的地方，让我们可以挑选出有哪些好玩的地方 #加载模块 import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from scipy.misc.pilutil import imread f = open(r'F:\spyder\kernel项目\pubg\PUBG_MatchData_Flattened.tsv')#添加路径 df = pd.read_csv(f,sep = '\t') #edf和mdf是两个地图，下面把两张地图分开进行处理 edf = df.loc[df['map_id'] == 'ERANGEL'] mdf = df.loc[df['map_id'] == 'MIRAMAR'] #print(edf.head()) def killer_victim_df_maker(df): #挑出地图中击杀和被杀玩家的坐标 df = edf victim_x_df = df.filter(regex = 'victim_position_x') victim_y_df = df.filter(regex = 'victim_position_y') killer_x_df = df.filter(regex = 'killer_position_x') killer_y_df = df.filter(regex = 'killer_position_y') #ravel()将多维矩阵变成一维 victim_x_s = pd.Series(victim_x_df.values.ravel('F')) victim_y_s = pd.Series(victim_y_df.values.ravel('F')) killer_x_s = pd.Series(killer_x_df.values.ravel('F')) killer_y_s = pd.Series(killer_y_df.values.ravel('F')) vdata = {'x':victim_x_s, 'y':victim_y_s} kdata = {'x':killer_x_s, 'y':killer_y_s} #dropna(how = 'any')删除带nan的行 #再留下坐标等于0（在边界上的异常数据）剔除 victim_df = pd.DataFrame(data = vdata).dropna(how = 'any') victim_df = victim_df[victim_df['x'] > 0] killer_df = pd.DataFrame(data = kdata).dropna(how = 'any') killer_df = killer_df[killer_df['x'] > 0] return killer_df, victim_df ekdf,evdf = killer_victim_df_maker(edf) mkdf,mvdf = killer_victim_df_maker(mdf) # print(ekdf.head())#在森林击杀的坐标数据 # print(evdf.head())#在森林被杀的坐标数据 # print(mkdf.head()) # print(mvdf.head()) # print(len(ekdf), len(evdf), len(mkdf), len(mvdf)) #将dataframe转换成numpy array plot_data_ev = evdf[['x','y']].values plot_data_ek = ekdf[['x','y']].values plot_data_mv = mvdf[['x','y']].values plot_data_mk = mkdf[['x','y']].values #将获得的坐标数据与地图上的坐标数据进行匹配 plot_data_ev = plot_data_ev * 4040 /800000 plot_data_ek = plot_data_ek * 4040 /800000 plot_data_mv = plot_data_mv * 976 /800000 plot_data_mk = plot_data_mk * 976 /800000 #加载模块 from scipy.ndimage.filters import gaussian_filter import matplotlib.cm as cm from matplotlib.colors import Normalize #热力图函数 def heatmap(x, y, s, bins = 100): # x = plot_data_ev[:,0] # y = plot_data_ev[:,1] # s = 1.5 # bins = 800 #np.histogram2d()将两列数值转为矩阵 heatmap, xedges, yedges = np.histogram2d(x, y, bins = bins) #高斯锐化模糊对象 heatmap = gaussian_filter(heatmap, sigma = s) extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]] return heatmap.T, extent #读取森林地图底图 #Normalize归一化 #np.clip(x,a,b)将x中小于a的值设为a，大于b的值设为b #cm.bwr 蓝白红 bg = imread('erangel.jpg') hmap, extent = heatmap(plot_data_ev[:,0], plot_data_ev[:,1], 1.5, bins =800) alphas = np.clip(Normalize(0, hmap.max()/100, clip=True)(hmap)*1.5,0.0,1.) colors = Normalize(hmap.max()/100, hmap.max()/20, clip=True)(hmap) colors = cm.bwr(colors) colors[..., -1] = alphas hmap2, extent2 = heatmap(plot_data_ek[:,0],plot_data_ek[:,1],1.5, bins = 800) alphas2 = np.clip(Normalize(0, hmap2.max()/100, clip = True)(hmap2)*1.5, 0.0, 1.) colors2 = Normalize(hmap2.max()/100, hmap2.max()/20, clip=True)(hmap2) colors2 = cm.RdBu(colors2) colors2[...,-1] = alphas2 #'森林死亡率图' fig, ax = plt.subplots(figsize = (24,24)) ax.set_xlim(0, 4096);ax.set_ylim(0, 4096) ax.imshow(bg) ax.imshow(colors, extent = extent, origin = 'lower', cmap = cm.bwr, alpha = 1) #ax.imshow(colors2, extent = extent2, origin = 'lower', cmap = cm.RdBu, alpha = 0.5) plt.gca().invert_yaxis() plt.title('森林地图死亡率图')
从图中可以看出来，港口，军事基地，p城，学校等这些红色的地方伤亡人数最大，而且很明显能看出下岛的两座桥上网率也很大，想来钢枪还是躲开人群都可以借鉴
#森林击杀图 fig, ax = plt.subplots(figsize = (24,24)) ax.set_xlim(0, 4096); ax.set_ylim(0, 4096) ax.imshow(bg) ax.imshow(colors2, extent = extent2, origin = 'lower', cmap = cm.RdBu, alpha = 1) plt.gca().invert_yaxis() plt.colorbar() plt.title('森林地图击杀率图')
这是森林击杀图，和森林死亡图基本重合，有人在的被击杀的地方就肯定有人击杀，不过要是仔细看的话，还有一点的差异，这便是LYB的藏身之地。
#沙漠地图 bg = imread('miramar.jpg') hmap, extent = heatmap(plot_data_mv[:,0], plot_data_mv[:,1], 1.5, bins = 800) alphas = np.clip(Normalize(0, hmap.max()/200, clip=True)(hmap2)*1.5, 0.0, 1.) colors2 = Normalize(hmap2.max()/100, hmap2.max()/20, clip=True)(hmap2) colors2 = cm.rainbow(colors2) colors2[..., -1] = alphas2 hmap2, extent2 = heatmap(plot_data_mv[:,0], plot_data_mv[:,1], 1.5, bins = 800) alphas2 = np.clip(Normalize(0, hmap2.max()/200, clip=True)(hmap2)*1.5, 0.0, 1.) colors = Normalize(hmap.max()/100, hmap.max()/20, clip=True)(hmap) colors = cm.rainbow(colors) colors[..., -1] = alphas a = colors2[...,-1] colors3 = colors2 colors3[...,-1] = np.clip(abs(colors2[...,-1]-colors[...,-1]),0.0,1.) np.mean(colors2[...,-1]-colors[...,-1])
#沙漠LYB图 fig, ax = plt.subplots(figsize = (24,24)) ax.set_xlim(0, 1000);ax.set_ylim(0, 1000) ax.imshow(bg) #ax.imshow(colors, extent = extent, origin = 'lower', cmap = cm.Blues, alpha = 0.5) ax.imshow(colors3, extent = extent2, origin = 'lower', cmap = cm.Reds, alpha = 0.5) plt.gca().invert_yaxis() plt.title('沙漠地图击杀率图') #通过对比击杀率和死亡率，寻找lyb藏身之地 #color3,击杀率大于死亡率的地方
''' 在查看上面的热图时，重要的是要记住蓝圈对整体死亡的影响。 地图中心区域的许多紫色斑点可能是由于区域向内推动玩家！ 同样，通过查看时间片可以稍微减轻这种情况。 我将创建另一个内核来执行此操作，因为它需要更改此内核的第一个代码块。 在此之前，让我们尝试查看每个垃圾箱中的杀死率。 首先，让我们定义一个除法函数，这样我们就不会除以0。 ''' def divbutnotbyzero(a, b): c = np.zeros(a.shape) for i, row in enumerate(b): for j, el in enumerate(row): if el == 0:#如果击杀率等于0的话 c[i][j] = a[i][j]#c值就等于死亡率的值 else: c[i][j] = a[i][j]/el#击杀/死亡 return c bg = imread('erangel.jpg') hmap, extent = heatmap(plot_data_ev[:,0], plot_data_ev[:,1], 0, bins = 800) hmap2, extent2 = heatmap(plot_data_ek[:,0], plot_data_ek[:,1], 0, bins = 800) hmap3 = divbutnotbyzero(hmap, hmap2) alphas = np.clip(Normalize(0, hmap3.max()/100, clip=True)(hmap)*1.5, 0.0,1.) colors = Normalize(hmap3.max()/100, hmap3.max()/20, clip=True)(hmap) colors = cm.rainbow(colors) colors[...,-1] = alphas fig, ax = plt.subplots(figsize = (24, 24)) ax.set_xlim(0,4096); ax.set_ylim(0, 4096) ax.imshow(bg) ax.imshow(colors, extent = extent, origin = 'lower', cmap = cm.rainbow, alpha = 0.5) plt.gca().invert_yaxis() ''' Pretty cool! Notably, the typical "hot zones" arent the only places for getting a good kill/death ratio. Anywhere that you are seeing red is a pretty good spot to land. Let's print the k/d mean: 太酷了！ 值得注意的是，典型的“热区”并不是获得良好杀伤/死亡率的唯一场所。 你看到红色的任何地方都是降落的好地方。 让我们打印k / d的意思是： ''' print(hmap3.mean()) bg = imread('miramar.jpg') hmap, extent = heatmap(plot_data_mv[:,0], plot_data_mv[:,1], 0, bins=800) hmap2, extent2 = heatmap(plot_data_mk[:,0], plot_data_mk[:,1], 0, bins=800) hmap3 = divbutnotbyzero(hmap,hmap2) alphas = np.clip(Normalize(0, hmap3.max()/100, clip=True)(hmap)*1.5, 0.0, 1.) colors = Normalize(hmap3.max()/100, hmap3.max()/20, clip=True)(hmap) colors = cm.rainbow(colors) colors[..., -1] = alphas fig, ax = plt.subplots(figsize=(24,24)) ax.set_xlim(0, 1000); ax.set_ylim(0, 1000) ax.imshow(bg) ax.imshow(colors, extent=extent, origin='lower', cmap=cm.rainbow, alpha=0.5) plt.gca().invert_yaxis()
