matplotlib 可视化

最新推荐文章于 2024-04-19 16:37:27 发布
最新推荐文章于 2024-04-19 16:37:27 发布
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本文链接：https://blog.youkuaiyun.com/qq_41198463/article/details/109579209
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本文详细探讨了matplotlib库在Python中的使用，包括创建各种图表，调整样式，自定义元素等，旨在帮助读者掌握高效的数据可视化技巧。

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import numpy as np
import matplotlib.pyplot as plt
import scipy.io as scio

def load_mat_file():
    map_data = '../query_gallery_compar/map.mat'
    rank1_data = '../query_gallery_compar/rank1.mat'
    data_map = scio.loadmat(map_data)
    data_rank1 = scio.loadmat(rank1_data)

    return data_map['mAP'], data_rank1['rank1']

def plot_curves(input_data):

    max_step = 3400
    legend_font = {'family' : 'Times New Roman',
                   'weight' : 'normal',
                   'size'   : 6,}
    label_font  = {'family' : 'Times New Roman',
                   'weight' : 'normal',
                   'size'   : 6,}
    title_font  = {'family' : 'Times New Roman',
                   'weight' : 'normal',
                   'size'   : 6,}
    ticks_font  = {'family' : 'Times New Roman',
                   'weight' : 'normal',
                   'size'   : 20,}
    img2img = 'Image-Image'
    img2vid = 'Image-Video'
    vid2img = 'Video-Image'
    vid2vid = 'Video-Video'


    color  =  {img2img:  'blue', # (99/255, 147/255, 112/255), # 'lightgreen',
               img2vid:   'burlywood',#(163/255, 163/255, 163/255),
               vid2img: 'blueviolet',  # (124/255, 72/255, 96/255),
               vid2vid: 'red', # (163/255, 163/255, 163/255)
               }
    line_style  =  {img2img: ':',
                    img2vid: '--',
                    vid2img: '-.', 
                    vid2vid: '-'
                    }
    marker_style  =  {img2img: '+',
                      img2vid: 'x',
                      vid2img: 'o',
                      vid2vid: '^'
                      }
    y_bottom = [0.65, 0.3, 0.3,0.3,0.65]
    y_top = [0.9, 0.85, 0.85, 0.85, 0.9]
    legend_ybot = [0.3, 0.2, 0.2]

    ###fig, axs = plt.subplots(2, 1, figsize=(15, 3.3))
    fig1, ax1 = plt.subplots(1, 1, figsize=(4, 4))
    fig2, ax2 = plt.subplots(1, 1, figsize=(4, 4))

    for ax, fig_name, fig_data in zip([1,2], input_data.keys(), input_data.values()):
        if ax == 1:
            ax = ax1
        else:
            ax = ax2
        sequence = [img2img, img2vid, vid2img, vid2vid]
        x = range(25,801,25)
        for i, label in enumerate(sequence):
            assert len(x) == len(fig_data[:, i])
            ax.plot(x, fig_data[:, i], label=label, color=color[label], linestyle = line_style[label], linewidth=1, marker = marker_style[label], markersize=4)
        ax.legend(prop = legend_font, edgecolor=(0, 0, 0), fancybox=False,loc= 2)
        # ax.set_title('{} Evaluation'.format(fig_name), fontdict=title_font)

        ax.spines['top'].set_linewidth(1)
        ax.spines['right'].set_linewidth(1)
        ax.spines['bottom'].set_linewidth(1); ###设置底部坐标轴的粗细
        ax.spines['left'].set_linewidth(1);   ####设置左边坐标轴的粗细
        ax.tick_params(direction='in', width=1)
        ###ax.grid(axis="y")
        ax.grid(True, 'major', 'x', ls='--', lw=.5, c='k', alpha=.3)
        ax.grid(True, 'major', 'y', ls='--', lw=.5, c='k', alpha=.3)
        ax.set_xlabel('Number of Epochs', fontdict=label_font)
        ax.set_ylabel('{}(%)'.format(fig_name), fontdict=label_font)
        ax.set_xlim(0, 820)
        ax.set_ylim(15, 50)
        ax.set_xticks(range(100, 801, 100))
        ax.tick_params(labelsize=12)
        #plt.xticks([4, 8, 12, 16, 20, 24, 28, 32], ['100', '200', '300', '400', '500', '600', '700', '800'])
        ###ax.set_xticks([4, 8, 12, 16, 20, 24, 28, 32], ['aa', 'bb', 'bb', '4bb00', '50bb0', '60bb0', '70bb0', 'bb'])
    # fig1.tight_layout() #调整整体空白

    plt.subplots_adjust(wspace =0, hspace =0.25)#调整子图间距
    plt.show()






if __name__ == '__main__':

    data_map, data_rank1 = load_mat_file()
    data=dict()
    data['mAP'] = data_map
    data['Rank-1'] = data_rank1
    plot_curves(data)

在这里插入图片描述
在这里插入图片描述

import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.pylab as pl
# from matplotlib.ticker import FuncFormatter   ### 今天的主角
import numpy as np
plt.rc('font',family='Times New Roman')

def main(data_path):
    results = pd.read_excel(data_path)
    h, w = results['h'].values, results['w'].values
    r_std, sqr_std = results['r_std'].values, results['sqr_std'].values
    recurs = results['recurrents'].values

    #读取个区间柱状图数量
    a1 = len(results.loc[results['sqr_std']<= 25])
    a2 = len(results.loc[(results['sqr_std']> 25 )&(results['sqr_std']<= 50)])
    a3 = len(results.loc[(results['sqr_std']> 50 )&(results['sqr_std']<= 75)])
    a4 = len(results.loc[(results['sqr_std']> 75 )&(results['sqr_std']<= 100)])
    a5 = len(results.loc[(results['sqr_std']> 100 )&(results['sqr_std']<= 125)])
    a6 = len(results.loc[(results['sqr_std']> 125 )&(results['sqr_std']<= 150)])
    a7 = len(results.loc[(results['sqr_std']> 150 )&(results['sqr_std']<= 175)])
    a8 = len(results.loc[results['sqr_std']> 175])
    b = a1+a2+a3+a4+a5+a6+a7+a8

    y1, y2= [],[]
    # 转换成百分比
    y1.append(a1/b)
    y1.append(a2/b)
    y1.append(a3/b)
    y1.append(a4/b)
    y1.append(a5/b)
    y1.append(a6/b)
    y1.append(a7/b)
    y1.append(a8/b)


    y2.append((b-a1)/ b)
    y2.append((b-a2)/ b)
    y2.append((b-a3)/ b)
    y2.append((b-a4)/ b)
    y2.append((b-a5)/ b)
    y2.append((b-a6)/ b)
    y2.append((b-a7)/ b)
    y2.append((b-a8)/ b)
    # print(y1, y2)
    x_data = ['0-25','25-50','50-75','75-100','100-125','125-150','150-175','>175']
    plt.ylim(0,0.5)
    # marks = ['*','*']
    # y = np.random.randint(20,180,len(marks))

    plt.bar(x = x_data, height=y1, color= 'b', alpha=0.8, )
    # plt.bar(x = x_data, height=y2,  color = 'r' ,alpha=0.8, bottom=y1)
    axes = plt.subplot(111)
    axes.bar(x = x_data, height = y2,  hatch='x', color='white', edgecolor='gray',bottom=y1, alpha=0.1)
    #绘制百分比
    for x, y in enumerate(y1):
        plt.text(x-0.35, 0.5, '%s' % format(y,'.2%'), va='top',color = 'black',rotation=30)
    plt.yticks([])
    axes.spines['top'].set_visible(False) #设置边框
    axes.spines['right'].set_visible(False)
    axes.spines['bottom'].set_visible(False)
    axes.spines['left'].set_visible(False)
    plt.xticks([0, 1, 2, 3, 4, 5, 6, 7],['0-25','25-50','50-75','75-100','100-125','125-150','150-175','>175'],rotation=30,fontproperties = 'Times New Roman', size = 14)
    plt.show()


    d1 = len(results.loc[results['recurrents']<= 1])
    d2 = len(results.loc[(results['recurrents']> 1 )&(results['recurrents']<= 2)])
    d3 = len(results.loc[(results['recurrents']> 2 )&(results['recurrents']<= 3)])
    d4 = len(results.loc[(results['recurrents']> 3 )&(results['recurrents']<= 4)])
    d5 = len(results.loc[(results['recurrents']> 4 )&(results['recurrents']<= 5)])
    d6 = len(results.loc[(results['recurrents']> 5 )&(results['recurrents']<= 6)])
    d7 = len(results.loc[(results['recurrents']> 6 )&(results['recurrents']<= 7)])
    d8 = len(results.loc[(results['recurrents']> 7 )&(results['recurrents']<= 8)])
    d9 = len(results.loc[results['recurrents']> 8])
    b2 = d1+d2+d3+d4+d5+d6+d7+d8+d9

    recurs1, recurs2 = [], []
    recurs1.append(d1/b2)
    recurs1.append(d2/b2)
    recurs1.append(d3/b2)
    recurs1.append(d4/b2)
    recurs1.append(d5/b2)
    recurs1.append(d6/b2)
    recurs1.append(d7/b2)
    recurs1.append(d8/b2)
    recurs1.append(d9/b2)


    recurs2.append((b2-d1)/ b2)
    recurs2.append((b2-d2)/ b2)
    recurs2.append((b2-d3)/ b2)
    recurs2.append((b2-d4)/ b2)
    recurs2.append((b2-d5)/ b2)
    recurs2.append((b2-d6)/ b2)
    recurs2.append((b2-d7)/ b2)
    recurs2.append((b2-d8)/ b2)
    recurs2.append((b2-d9)/ b2)
    # print(y1, y2)
    x_data_recurs = ['1','2','3','4','5','6','7','8','9']
    plt.ylim(0,0.7)


    plt.bar(x = x_data_recurs, height=recurs1, color= 'b', alpha=0.8, )
    # plt.bar(x = x_data, height=y2,  color = 'r' ,alpha=0.8, bottom=y1)
    axes = plt.subplot(111)
    axes.bar(x = x_data_recurs, height = recurs2,  hatch='x', color='white', edgecolor='gray',bottom=recurs1, alpha=0.1)
    for x, y in enumerate(recurs1):
        plt.text(x-0.35, 0.7, '%s' % format(y,'.2%'), va='top',color = 'black',rotation=30)
    plt.yticks([])
    axes.spines['top'].set_visible(False)
    axes.spines['right'].set_visible(False)
    axes.spines['bottom'].set_visible(False)
    axes.spines['left'].set_visible(False)
    plt.xticks([0, 1, 2, 3, 4, 5, 6, 7, 8],['1','2','3','4','5','6','7','8','9'],rotation=30,fontproperties = 'Times New Roman', size = 14)
    plt.show()


    c1 = len(results.loc[results['r_std']<= 0.1])
    c2 = len(results.loc[(results['r_std']> 0.1 )&(results['r_std']<= 0.2)])
    c3 = len(results.loc[(results['r_std']> 0.2 )&(results['r_std']<= 0.3)])
    c4 = len(results.loc[(results['r_std']> 0.3 )&(results['r_std']<= 0.4)])
    c5 = len(results.loc[(results['r_std']> 0.4 )&(results['r_std']<= 0.5)])
    c6 = len(results.loc[(results['r_std']> 0.5 )&(results['r_std']<= 0.6)])
    c7 = len(results.loc[results['r_std']> 0.6])
    b3 = c1+c2+c3+c4+c5+c6+c7

    r_std1, r_std2 = [], []
    r_std1.append(c1/b3)
    r_std1.append(c2/b3)
    r_std1.append(c3/b3)
    r_std1.append(c4/b3)
    r_std1.append(c5/b3)
    r_std1.append(c6/b3)
    r_std1.append(c7/b3)



    r_std2.append((b3-c1)/ b3)
    r_std2.append((b3-c2)/ b3)
    r_std2.append((b3-c3)/ b3)
    r_std2.append((b3-c4)/ b3)
    r_std2.append((b3-c5)/ b3)
    r_std2.append((b3-c6)/ b3)
    r_std2.append((b3-c7)/ b3)

    # print(y1, y2)
    x_data_recurs = ['0-0.1','0.1-0.2','0.2-0.3','0.3-0.4','0.4-0.5','0.5-0.6','0.6-0.7']
    plt.ylim(0,0.6)


    plt.bar(x = x_data_recurs, height=r_std1, color= 'b', alpha=0.8, )
    # plt.bar(x = x_data, height=y2,  color = 'r' ,alpha=0.8, bottom=y1)
    axes = plt.subplot(111)
    axes.bar(x = x_data_recurs, height = r_std2,  hatch='x', color='white', edgecolor='gray',bottom=r_std1, alpha=0.1)
    for x, y in enumerate(r_std1):
        plt.text(x-0.35, 0.6, '%s' % format(y,'.2%'), va='top',color = 'black',rotation=30)
    plt.yticks([])
    axes.spines['top'].set_visible(False)
    axes.spines['right'].set_visible(False)
    axes.spines['bottom'].set_visible(False)
    axes.spines['left'].set_visible(False)
    plt.xticks([0, 1, 2, 3, 4, 5, 6],['0-0.1','0.1-0.2','0.2-0.3','0.3-0.4','0.4-0.5','0.5-0.6','0.6-0.7'],rotation=30,fontproperties = 'Times New Roman', size = 14)
    plt.show()
    # #


    # bin_range = [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7]
    # r_std[r_std>=0.7] = 0.7
    # plt.hist(r_std, bins=bin_range, rwidth=0.5, align='left')
    # plt.yticks(fontproperties = 'Times New Roman', size = 14)
    # plt.xticks(fontproperties = 'Times New Roman', size = 14)
    # plt.grid(axis='y', linestyle='--')
    # plt.xticks([0,0.1,0.2,0.3,0.4,0.5,0.6],['0-0.1','0.1-0.2','0.2-0.3','0.3-0.4','0.4-0.5','0.5-0.6','>0.6'])
    # pl.xticks(rotation=30)
    # # plt.legend(r'$\sigma_{\sqrt{\mathrm{H}/\mathrm{W}}}$')
    # plt.xlabel('(c) Sequence ratio variation distribution')
    # plt.show()
    # # #
    # # #

if __name__ == '__main__':

    data_path = './data.xls'
    main(data_path)





import numpy as np
import matplotlib.pyplot as plt
import os.path as osp
import os
import glob
from PIL import Image
import matplotlib.pylab as pl
import seaborn as sns




def cam_seqlen(data_path):
    num_ids_ge_2 = 0           # the number of tracklets that are captured by at least two cameras
    num_imgs = 0               # the number of bounding boxes
    cam_seq_len = {}
    recurrents = []
    img_areas, img_hs, img_ws, img_rs = [], [], [], []
    seq_areas_std, seq_hs_std, seq_ws_std, seq_rs_std = [], [], [], []

    id_paths = glob.glob(os.path.join(data_path, '*'))
    
    for id_path in id_paths:
        seq_id = id_path.split('/')[-1]
        seq_paths = glob.glob(os.path.join(id_path, '*'))
        recurrents.append(len(seq_paths))
        if len(seq_paths) >= 2:
            num_ids_ge_2 += 1
            for seq_path in seq_paths:
                seq_imgs_area, seq_imgs_h, seq_imgs_w, seq_imgs_r = [], [], [], []
                lid, cid, vid = seq_path.split('/')[-1].split('_')
                img_paths = glob.glob(os.path.join(seq_path, '*.png'))
                # for img_path in img_paths:
                #     img = Image.open(img_path)
                #     width, height = img.size
                #     seq_imgs_area.append(width * height)
                #     seq_imgs_h.append(height)
                #     seq_imgs_w.append(width)
                #     seq_imgs_r.append(width/height)
                # img_areas.extend(seq_imgs_area)
                # img_hs.extend(seq_imgs_h)
                # img_ws.extend(seq_imgs_w)
                # img_rs.extend(seq_imgs_r)

                # seq_areas_std.append(np.std(seq_imgs_area))
                # seq_hs_std.append(np.std(seq_imgs_h))
                # seq_ws_std.append(np.std(seq_imgs_w))
                # seq_rs_std.append(np.std(seq_imgs_r))

            


                seq_len = len(img_paths)
                if lid in cam_seq_len.keys():
                    cam_seq_len[lid].append(seq_len)
                else:
                    cam_seq_len[lid] = []
                    cam_seq_len[lid].append(seq_len)

    
    
    plt.rc('font',family='Times New Roman')
    # plt.hist(recurrents, bins=40)
    # plt.show()
    # for lid in cam_seq_len.keys():
    # n2, bins2, patches2 = plt.hist([cam_seq_len['L02'], cam_seq_len['L04'], cam_seq_len['L01']], bins=25, histtype='bar', align='left')
    # print(n2.shape, bins2.shape)
    # fig = plt.figure()
    # fig, ax = plt.subplots(1,1)
    
    plt.hist([cam_seq_len['L01'], cam_seq_len['L02'], cam_seq_len['L04']], bins=15, histtype='bar', align='left', density=True, color=['r','blue','green'], alpha=0.3)
    plt.yticks(size = 14)
    plt.xticks(size = 14)
    sns.kdeplot(cam_seq_len['L01'], shade=False, color='r', shade_lowest=True)
    sns.kdeplot(cam_seq_len['L02'], shade=False, color='blue', shade_lowest=True)
    sns.kdeplot(cam_seq_len['L04'], shade=False, color='green',shade_lowest=True)
    # sns.distplot(cam_seq_len, x=['L02', 'L04', 'L01'], bins=25,  kde=True)
    # plt.xlabel(lid)
    # plt.plot(bins2[:-1],n2[0],'--')

    plt.legend(['Camear01','Camear02','Camera04'],loc = 0)
  
    plt.grid(axis='y', linestyle='--')
    plt.show()
    
    


    # all_imgs = [img_areas, img_hs, img_ws, img_rs]
    # all_imgs_label = ['a', 'h', 'w', 'r']

    # all_seqs = [seq_areas_std, seq_hs_std, seq_ws_std, seq_rs_std]
    # all_seqs_label = ['as', 'hs', 'ws', 'rs']

    # for i, all_img in enumerate(all_imgs):
    #     plt.hist(all_img, bins=40)
    #     plt.xlabel(all_imgs_label[i])
    #     plt.show()

    # for i, all_seq in enumerate(all_seqs):
    #     plt.hist(all_seq, bins=40)
    #     plt.xlabel(all_seqs_label[i])
    #     plt.show()

    

    
    # print("The ids in VVReID is {}".format(num_ids_ge_2))

if __name__ == "__main__":
    cam_seqlen('/run/user/1000/gvfs/smb-share:server=192.168.20.195,share=benchmark/download_dataset/VVReID_PaperVersion_NoMask/IDs')






![在这里插入图片描述](https://img-blog.csdnimg.cn/20201109155418247.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTk4NDYz,size_16,color_FFFFFF,t_70#pic_center)
![在这里插入图片描述](https://img-blog.csdnimg.cn/20201109155428836.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTk4NDYz,size_16,color_FFFFFF,t_70#pic_center)
![在这里插入图片描述](https://img-blog.csdnimg.cn/20201109155517265.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTk4NDYz,size_16,color_FFFFFF,t_70#pic_center)
![在这里插入图片描述](https://img-blog.csdnimg.cn/20201109155525977.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTk4NDYz,size_16,color_FFFFFF,t_70#pic_center)
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本文详细介绍了Python数据分析的基础知识及其环境构建，重点讲解了Matplotlib这个强大的数据可视化工具，通过具体示例演示了各类常见图表如折线图、柱状图、散点图的操作方法，深入介绍了定制图表、子图布局和互动性...
Matplotlib可视化绘图（数据进行可视化）
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Matplotlib可视化绘图（数据进行可视化）
Python数据分析与可视化项目电商天猫双十一美妆销售数据分析约400行matplotlib可视化数据分析
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本项目利用Python编程语言，特别是其强大的matplotlib可视化库，深入分析了天猫双十一美妆产品的销售数据，揭示了产品销售的趋势和消费者偏好等信息。 项目的主体内容包含对数据的采集、清洗、处理、分析和可视化...
"import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt"
weixin_42587866的博客
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这三行代码都是在导入 Python 中的三个库： "import numpy as np"：这行代码导入了 numpy 库，并将其简写为 np。numpy 是一个用于科学计算的 Python 库，提供了大量的数学函数和矩阵操作，常用于机器学习、数据分析等领域。 "import pandas as pd"：这行代码导入了 pandas 库，并将其简写为 pd。pandas 是一个用于数据处理和分...
机器学习笔记--python之scipy
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学习了numpy和matplotlib，基本上线性代数，概率论的很多计算啊之类的都可以很容易的实现了。此外再学习下scipy这个科学函数库吧。 scipy包包含致力于科学计算中常见问题的各个工具箱。它的不同子模块相应于不同的应用。像插值，积分，优化，图像处理，特殊函数等等。
python作业——Matplotlib
pp_zqh的博客
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Exercise11.1 description: code: import numpy as np import matplotlib.pyplot as plt x = np. linspace (0, 2, 10000) y = (np.sin(x - 2) ** 2) * np.exp(-1 * (x**2)) plt.xlim(0, 2) plt.ylim(0, 1) pl...
Python安装时import matplotlib.pyplot as plt报错
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yang6464158的专栏
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安装matplotlib的时候可能会出现输入import matplotlib.pyplot as plt出现报错的现象，如下图所示： >>> import matplotlib >>> import numpy >>> import scipy >>> import pyparsing >>> import matplotlib.pyplot as plt Traceback
Numpy快速入门教程（三）：SciPy，Matplotlib
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首先声明本篇博客是本人学习CS231n的学习笔记，分享给大家当作参考。SciPyNumpy提供了高性能的多维数组，以及计算和操作数组的基本工具。SciPy基于Numpy，提供了大量的计算和操作数组的函数，这些函数对于不同类型的科学和工程计算非常有用。熟悉SciPy的最好方法就是阅读文档。我们会强调对于本课程有用的部分。图像操作SciPy提供了一些操作图像的基本函数。比如，它提供了将图像从硬盘读入到数
import numpy as np_NumPy：创建和操作数值数据
weixin_39876002的博客
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作者：Emmanuelle Gouillart、Didrik Pinte、Gaël Varoquaux 和 Pauli Virtanen本章给出关于Numpy概述，Numpy是Python中高效数值计算的核心工具。1.3.1 Numpy 数组对象1.3.1.1 什么是Numpy以及Numpy数组？1.3.1.1.1 Numpy数组Python对象：高级数值对象：整数、浮点容器：列表(无成...
python数据分析scipy和matplotlib（三）
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Scipy 在numpy基础上增加了众多的数学、科学及工程常用的库函数； 线性代数、常微分方程求解、信号处理、图像处理、稀疏矩阵等；   Matplotlib 用于创建出版质量图表的绘图工具库； 目的是为python构建一个Matlab式的绘图接口； import matplotlib.pyplot as plt，pyplot模块包含了常用的matplotlib API函数； figure，...
matplotlib绘制数学曲线
weixin_45080737的博客
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matplotlib绘制数学曲线, 简单,高效 辛苦一下下,换来专属数学曲线好图
Python数据分析：世界发展指数的Matplotlib可视化
资源摘要信息: "使用Matplotlib对世界发展指数数据集进行可视化分析（包含源码及数据集）" 知识点详细说明： 1. Python编程语言：Python是一种广泛应用于数据科学、机器学习、网络开发、自动化等领域的高级编程...
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