全网最全的平行坐标图(parallel coordinates plot)的绘制攻略

早上起来拥抱太阳，写小论文，看到人家的图怎么那么好看！！？？
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这不得赶紧抄下来，我也发一个顶刊？于是开始思考如何解决绘制这个问题，目前现有的大部分解决方案都是直接调库，查了一下现有的所有解决方案，

python_4">1.直接python调库

这篇文章直接调库，但是没有分类，而且也感觉不太好看
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https://www.zhihu.com/question/571887313

这一个是调pandas库的，虽然有分类但是他的轴不能归一化调整。
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https://www.cnblogs.com/caiyishuai/p/12322671.html

2.matlab

matlab作为强大的科研工具是众所周知的，他也提供了一个库来专门绘制平行坐标图。但是由于电脑内存不够，我就直接用线上matlab。经过学习之后，绘制出来这样的图，emmm老实说有点丑，但是指不定有人需要这个呢？
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tbl = readtable('tt.csv');
head(tbl);
tbl.Name = categorical(tbl.Name);
p = parallelplot(tbl);
p.GroupVariable = 'Name';
p.CoordinateVariables = [3 2 4];
p.LineWidth = 1.5;
p.FontSize = 25;
p.Color = {'#B4FF00','#00E68C','#1432FA','#B4C8FA','#FF3232'}
%p.Color = {'#780001','#C11221','#FEF0D5','#002F49','#669BBB'}

其中csv的格式大概是这样的

cost time energy Name
0 2 20 GMPSO
0 8 90 KAMSA
6 5 30 COSA

太丑了还是下一个

3.用Origin绘制

Origin不愧是专业的绘图工具，绘制出来的图确实还不错。这里也是因为电脑内存原因所以我就没试着用Origin了。贴个参考文献：

https://cloud.tencent.com/developer/article/1623006?areaSource=102001.5&traceId=e-JyHo2xQfKU1fPJYmtbA

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4.用高手做的轮子

上面尽管提供了三种方案，但是感觉也不能绘制出我想要的图形。于是乎我就上github寻找，肯定有大神。这个大神是我目前找到最满意的解决方案了。

https://github.com/jraine/parallel-coordinates-plot-dataframe

这个仓库提供了一个不错的解决方案，他能绘制出好看的平行坐标图，而且也不用引太多的库。

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照着这个代码魔改了一下，我就绘制出来这样的图形了

这不比上面的图要好看？我就是天才啊哈哈哈，现在先问问老师我最后一格要不要换成图例，不用的话论文就贴这个图了。下面是魔改后的代码：

python">import matplotlib
from matplotlib import ticker
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.interpolate import make_interp_splinedef read_and_add_method(file_path, method_name):df = pd.read_csv(file_path)if method_name == 'ChaoticOSA':method_name = 'COSA'if method_name == 'WDNMN':method_name = 'WDNS'df['method'] = method_name  # 添加新列记录算法名return dfdef parallel_plot(df,cols,rank_attr,cmap='Spectral',spread=False,curved=0.1,curvedextend=0.05):'''Produce a parallel coordinates plot from pandas dataframe with line colour with respect to a column.Required Arguments:df: dataframecols: columns to use for axesrank_attr: attribute to use for rankingOptions:cmap: Colour palette to use for ranking of linesspread: Spread to use to separate lines at categorical valuescurved: Spline interpolation along linescurvedextend: Fraction extension in y axis, adjust to contain curvatureReturns:x coordinates for axes, y coordinates of all lines'''colmap = matplotlib.cm.get_cmap(cmap)cols = cols + [rank_attr]fig, axes = plt.subplots(1, len(cols)-1, sharey=False, figsize=(3*len(cols)+3,5))#绘制三个子图valmat = np.ndarray(shape=(len(cols),len(df)))#定义需要绘制曲线的数组有df行，cols列x = np.arange(0,len(cols),1)#貌似没什么用,有3列那么x=[0,1,2]ax_info = {}for i,col in enumerate(cols):#归一化数据vals = df[col]if (vals.dtype == float) & (len(np.unique(vals)) > 20):minval = np.min(vals)maxval = np.max(vals)rangeval = maxval - minval#区间长度vals = np.true_divide(vals - minval, maxval-minval)#归一化处理vals-minval/maxval-minval除法运算nticks = 5tick_labels = [round(minval + i*(rangeval/nticks),4) for i in range(nticks+1)]ticks = [0 + i*(1.0/nticks) for i in range(nticks+1)]valmat[i] = valsax_info[col] = [tick_labels,ticks]else:vals = vals.astype('category')#假如是目录型cats = vals.cat.categoriesc_vals = vals.cat.codesminval = 0maxval = len(cats)-1if maxval == 0:c_vals = 0.5else:c_vals = np.true_divide(c_vals - minval, maxval-minval)tick_labels = catsticks = np.unique(c_vals)ax_info[col] = [tick_labels,ticks]if spread is not None:offset = np.arange(-1,1,2./(len(c_vals)))*2e-2np.random.shuffle(offset)c_vals = c_vals + offsetvalmat[i] = c_valsextendfrac = curvedextend if curved else 0.05  for i,ax in enumerate(axes):for idx in range(valmat.shape[-1]):if curved:x_new = np.linspace(0, len(x), len(x)*20)a_BSpline = make_interp_spline(x, valmat[:,idx],k=3,bc_type='clamped')y_new = a_BSpline(x_new)ax.plot(x_new,y_new,color=colmap(valmat[-1,idx]),alpha=0.5)else:ax.plot(x,valmat[:,idx],color=colmap(valmat[-1,idx]),alpha=0.5)ax.set_ylim(0-extendfrac,1+extendfrac)ax.set_xlim(i,i+1)for dim, (ax,col) in enumerate(zip(axes,cols)):ax.xaxis.set_major_locator(ticker.FixedLocator([dim]))ax.yaxis.set_major_locator(ticker.FixedLocator(ax_info[col][1]))ax_info[col][0] = [int(label) for label in ax_info[col][0]]#y标签下取整ax.set_yticklabels(ax_info[col][0])ax.set_xticklabels([cols[dim]])plt.subplots_adjust(wspace=0)norm = matplotlib.colors.Normalize(0,1)#*axes[-1].get_ylim())sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)cbar = plt.colorbar(sm,pad=0,ticks=ax_info[rank_attr][1],extend='both',extendrect=True,extendfrac=extendfrac)#if curved:#cbar.ax.set_ylim(0-curvedextend,1+curvedextend)cbar.ax.set_yticklabels(ax_info[rank_attr][0])cbar.ax.set_xlabel(rank_attr)plt.show()return x,valmatmethod_names = ['GALCS','GMPSO',"ChaoticOSA","KAMSA","WDNMN"]
data_size = 30
data_index = 50
data_set_name = 'CyberShake'# 存储所有DataFrame的列表
all_dataframes = []# 遍历目录下所有的CSV文件
for method_name in method_names:fileName = 'D://demo//dataset/5.0-5.0/'+str(data_size)+'/'+str(data_size)+data_set_name+str(data_index)+method_name+'_0.csv'df = read_and_add_method(fileName, method_name)# 将DataFrame添加到列表中all_dataframes.append(df)# 将所有的DataFrame拼接在一起
final_dataframe = pd.concat(all_dataframes, ignore_index=True)
print(final_dataframe)parallel_plot(final_dataframe,['time','cost','energy'],'method')
# 定义函数，读取CSV文件并添加一个新列'method'