文章目錄

• 前言
• 1 Figure和Axes的文本
• • 1.1 text
  • 1.2 title和set_title
  • 1.3 figtext和text
  • 1.4 suptitle
  • 1.5 xlabel和ylabel
  • 1.6 annotate
  • 1.7 字體的設(shè)置方法
  • 總結(jié)：1.1~1.7知識點匯總
  • 1.8 數(shù)學表達式
• 2 tick上的text
• • 2.1 Tick Locators and Formatters
  • • 2.1.1 Formatters
    • 2.1.2 Locator
    • 2.1.3 調(diào)整x、y軸的位置
• 3 Legend
• 作業(yè)

前言

本博客是Task04的筆記。

---

1 Figure和Axes的文本

1.1 text

說明：

參數(shù)：此方法接受以下描述的參數(shù)：
s:此參數(shù)是要添加的文本。
xy:此參數(shù)是放置文本的點(x，y)。
fontdict:此參數(shù)是一個可選參數(shù)，并且是一個覆蓋默認文本屬性的字典。如果fontdict為None，則由rcParams確定默認值。
返回值：此方法返回作為創(chuàng)建的文本實例的文本。

#fontdict學習的案例 #學習的過程中請嘗試更換不同的fontdict字典的內(nèi)容，以便于更好的掌握 import numpy as np import matplotlib.pyplot as plt #---------設(shè)置字體樣式，分別是字體，顏色，寬度，大小 font1 = {'family': 'Times New Roman','color': 'purple','weight': 'normal','size': 16,} font2 = {'family': 'Times New Roman','color': 'red','weight': 'normal','size': 16,} font3 = {'family': 'serif','color': 'blue','weight': 'bold','size': 14,} font4 = {'family': 'Calibri','color': 'navy','weight': 'normal','size': 17,} #-----------四種不同字體顯示風格-----#-------建立函數(shù)---------- x = np.linspace(0.0, 5.0, 100) y = np.cos(2*np.pi*x) * np.exp(-x/3) #-------繪制圖像，添加標注---------- plt.plot(x, y, '--') plt.title('Damped exponential decay', fontdict=font1) #------添加文本在指定的坐標處------------ plt.text(2, 0.65, r'$\cos(2 \pi x) \exp(-x/3)$', fontdict=font2) #---------設(shè)置坐標標簽 plt.xlabel('Y=time (s)', fontdict=font3) plt.ylabel('X=voltage(mv)', fontdict=font4)# 調(diào)整圖像邊距 plt.subplots_adjust(left=0.15) plt.show()

給test加個框框：

import matplotlib.pyplot as pltplt.text(0.6, 0.7, "I ?", size=50, rotation=30.,ha="center", va="center",bbox=dict(boxstyle="round", # bbox：以框框形式輸出，要帶上參數(shù)ec=(1., 0.5, 0.5), # edgecolorfc=(1., 0.8, 0.8), # facecoloor))plt.text(0.8, 0.7, "Datawhale", size=40, rotation=-25.,ha="right", va="top",bbox=dict(boxstyle="square", # bbox：以框框形式輸出，要帶上參數(shù)ec=(1., 0.5, 0.5), # edgecolorfc=(1., 0.8, 0.8), # facecoloor))plt.show()

1.2 title和set_title

pyplot API：matplotlib.pyplot.title(label, fontdict=None, loc=None, pad=None, *, y=None, **kwargs)
OO API:Axes.set_title(self, label, fontdict=None, loc=None, pad=None, *, y=None, **kwargs)

import matplotlib.pyplot as pltlabels = ['G1', 'G2', 'G3', 'G4', 'G5'] men_means = [20, 35, 30, 35, 27] women_means = [25, 32, 34, 20, 25] men_std = [2, 3, 4, 1, 2] women_std = [3, 5, 2, 3, 3] width = 0.35 # the width of the bars: can also be len(x) sequencefig, ax = plt.subplots()ax.bar(labels, men_means, width, yerr=men_std, label='Men') ax.bar(labels, women_means, width, yerr=women_std, bottom=men_means,label='Women')ax.set_ylabel('Scores') ax.set_title('Scores by group and gender') ax.legend()plt.show()

1.3 figtext和text

pyplot API：matplotlib.pyplot.figtext(x, y, s, fontdict=None, **kwargs)
OO API:text(self, x, y, s, fontdict=None,**kwargs)

1.4 suptitle

調(diào)用方式如下：

fig.suptitle('This is the figure title', fontsize=12) plt.suptitle("GridSpec Inside GridSpec")

1.5 xlabel和ylabel

pyplot API：
matplotlib.pyplot.xlabel(xlabel, fontdict=None, labelpad=None, , loc=None, **kwargs)
matplotlib.pyplot.ylabel(ylabel, fontdict=None, labelpad=None,, loc=None, **kwargs)
OO API:
Axes.set_xlabel(self, xlabel, fontdict=None, labelpad=None, , loc=None, **kwargs)
Axes.set_ylabel(self, ylabel, fontdict=None, labelpad=None,, loc=None, **kwargs)

兩種調(diào)用方式：

import numpy as np import matplotlib.pyplot as pltfont = {'family': 'serif','color': 'darkred','weight': 'normal','size': 16,}x = np.linspace(0.0, 5.0, 100) y = np.cos(2*np.pi*x) * np.exp(-x)plt.plot(x, y, 'k') plt.title('Damped exponential decay', fontdict=font) plt.text(2, 0.65, r'$\cos(2 \pi t) \exp(-t)$', fontdict=font) plt.xlabel('time (s)', fontdict=font) plt.ylabel('voltage (mV)', fontdict=font)# Tweak spacing to prevent clipping of ylabel plt.subplots_adjust(left=0.15) plt.show()

#文本屬性的輸入一種是通過**kwargs屬性這種方式，一種是通過操作 matplotlib.font_manager.FontProperties 方法 #該鏈接是FontProperties方法的介紹 https://matplotlib.org/api/font_manager_api.html#matplotlib.font_manager.FontProperties from matplotlib.font_manager import FontProperties import matplotlib.pyplot as plt import numpy as npx1 = np.linspace(0.0, 5.0, 100) y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)font = FontProperties() font.set_family('serif') font.set_name('Times New Roman') font.set_style('italic')fig, ax = plt.subplots(figsize=(5, 3)) fig.subplots_adjust(bottom=0.15, left=0.2) ax.plot(x1, y1) ax.set_xlabel('time [s]', fontsize='large', fontweight='bold') ax.set_ylabel('Damped oscillation [V]', fontproperties=font)plt.show()

1.6 annotate

pyplot API：matplotlib.pyplot.annotate(text, xy, *args,**kwargs)
OO API:Axes.annotate(self, text, xy, *args,**kwargs)

參數(shù)：
text:str，該參數(shù)是指注釋文本的內(nèi)容
xy：該參數(shù)接受二維元組(float, float)，是指要注釋的點。其二維元組所在的坐標系由xycoords參數(shù)決定
xytext：注釋文本的坐標點，也是二維元組，默認與xy相同

import numpy as np import matplotlib.pyplot as pltfig, ax = plt.subplots()t = np.arange(0.0, 5.0, 0.01) s = np.cos(2*np.pi*t) line, = ax.plot(t, s, lw=2)ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),arrowprops=dict(facecolor='black', shrink=0.05),) ax.set_ylim(-2, 2) plt.show()

import matplotlib.pyplot as plt def demo_con_style(ax, connectionstyle):x1, y1 = 0.3, 0.2x2, y2 = 0.8, 0.6ax.plot([x1, x2], [y1, y2], ".")ax.annotate("",xy=(x1, y1), xycoords='data',xytext=(x2, y2), textcoords='data',arrowprops=dict(arrowstyle="->", color="0.5",shrinkA=5, shrinkB=5,patchA=None, patchB=None,connectionstyle=connectionstyle,),)ax.text(.05, .95, connectionstyle.replace(",", ",\n"),transform=ax.transAxes, ha="left", va="top")fig, axs = plt.subplots(3, 5, figsize=(8, 4.8)) demo_con_style(axs[0, 0], "angle3,angleA=90,angleB=0") demo_con_style(axs[1, 0], "angle3,angleA=0,angleB=90") demo_con_style(axs[0, 1], "arc3,rad=0.") demo_con_style(axs[1, 1], "arc3,rad=0.3") demo_con_style(axs[2, 1], "arc3,rad=-0.3") demo_con_style(axs[0, 2], "angle,angleA=-90,angleB=180,rad=0") demo_con_style(axs[1, 2], "angle,angleA=-90,angleB=180,rad=5") demo_con_style(axs[2, 2], "angle,angleA=-90,angleB=10,rad=5") demo_con_style(axs[0, 3], "arc,angleA=-90,angleB=0,armA=30,armB=30,rad=0") demo_con_style(axs[1, 3], "arc,angleA=-90,angleB=0,armA=30,armB=30,rad=5") demo_con_style(axs[2, 3], "arc,angleA=-90,angleB=0,armA=0,armB=40,rad=0") demo_con_style(axs[0, 4], "bar,fraction=0.3") demo_con_style(axs[1, 4], "bar,fraction=-0.3") demo_con_style(axs[2, 4], "bar,angle=180,fraction=-0.2")for ax in axs.flat:ax.set(xlim=(0, 1), ylim=(0, 1), xticks=[], yticks=[], aspect=1) fig.tight_layout(pad=0.2)plt.show()

#以下兩個block懂了之后，annotate基本懂了 #如果想更深入學習可以參看官網(wǎng)案例學習https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.annotate.html#matplotlib.axes.Axes.annotate import numpy as np import matplotlib.pyplot as plt# 以步長0.005繪制一個曲線 x = np.arange(0, 10, 0.005) y = np.exp(-x/2.) * np.sin(2*np.pi*x)fig, ax = plt.subplots() ax.plot(x, y) ax.set_xlim(0, 10)#設(shè)置x軸的范圍 ax.set_ylim(-1, 1)#設(shè)置x軸的范圍# 被注釋點的數(shù)據(jù)軸坐標和所在的像素 xdata, ydata = 5, 0 xdisplay, ydisplay = ax.transData.transform_point((xdata, ydata))# 設(shè)置注釋文本的樣式和箭頭的樣式 bbox = dict(boxstyle="round", fc="0.8") arrowprops = dict(arrowstyle = "->",connectionstyle = "angle,angleA=0,angleB=90,rad=10")# 設(shè)置偏移量 offset = 72 # xycoords默認為'data'數(shù)據(jù)軸坐標，對坐標點（5,0）添加注釋 # 注釋文本參考被注釋點設(shè)置偏移量，向左2*72points，向上72points ax.annotate('data = (%.1f, %.1f)'%(xdata, ydata),(xdata, ydata), xytext=(-2*offset, offset), textcoords='offset points',bbox=bbox, arrowprops=arrowprops)# xycoords以繪圖區(qū)左下角為參考，單位為像素 # 注釋文本參考被注釋點設(shè)置偏移量，向右0.5*72points，向下72points disp = ax.annotate('display = (%.1f, %.1f)'%(xdisplay, ydisplay),(xdisplay, ydisplay), xytext=(0.5*offset, -offset),xycoords='figure pixels',textcoords='offset points',bbox=bbox, arrowprops=arrowprops) plt.show()

關(guān)于參數(shù)xycoords：

ValueDescription

‘figure points’	Points from the lower left of the figure
‘figure pixels’	Pixels from the lower left of the figure

import numpy as np import matplotlib.pyplot as plt# 繪制一個極地坐標，再以0.001為步長，畫一條螺旋曲線 fig = plt.figure() ax = fig.add_subplot(111, polar=True) r = np.arange(0,1,0.001) theta = 2 * 2*np.pi * r line, = ax.plot(theta, r, color='#ee8d18', lw=3)# 對索引為800處畫一個圓點，并做注釋 ind = 800 thisr, thistheta = r[ind], theta[ind] ax.plot([thistheta], [thisr], 'o') ax.annotate('a polar annotation',xy=(thistheta, thisr), # 被注釋點遵循極坐標系，坐標為角度和半徑xytext=(0.05, 0.05), # 注釋文本放在繪圖區(qū)的0.05百分比處textcoords='figure fraction',arrowprops=dict(facecolor='black', shrink=0.05),# 箭頭線為黑色，兩端縮進5%horizontalalignment='left',# 注釋文本的左端和低端對齊到指定位置verticalalignment='bottom',) plt.show()

1.7 字體的設(shè)置方法

• 全局字體設(shè)置

#該block講述如何在matplotlib里面，修改字體默認屬性，完成全局字體的更改。 import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimSun'] # 指定默認字體為新宋體。 plt.rcParams['axes.unicode_minus'] = False # 解決保存圖像時 負號'-' 顯示為方塊和報錯的問題。

• 局部字體設(shè)置

#局部字體的修改方法1 import matplotlib.pyplot as plt import matplotlib.font_manager as fontmgx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] plt.plot(x, label='小示例圖標簽')# 直接用字體的名字。 plt.xlabel('x 軸名稱參數(shù)', fontproperties='Microsoft YaHei', fontsize=16) # 設(shè)置x軸名稱，采用微軟雅黑字體 plt.ylabel('y 軸名稱參數(shù)', fontproperties='Microsoft YaHei', fontsize=14) # 設(shè)置Y軸名稱 plt.title('坐標系的標題', fontproperties='Microsoft YaHei', fontsize=20) # 設(shè)置坐標系標題的字體 plt.legend(loc='lower right', prop={"family": 'Microsoft YaHei'}, fontsize=10) # 小示例圖的字體設(shè)置

總結(jié)：1.1~1.7知識點匯總

#這是以上學習內(nèi)容的總結(jié)案例 import matplotlib import matplotlib.pyplot as pltfig = plt.figure() ax = fig.add_subplot(111) fig.subplots_adjust(top=0.85)# Set titles for the figure and the subplot respectively fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold') ax.set_title('axes title')ax.set_xlabel('xlabel') ax.set_ylabel('ylabel')# Set both x- and y-axis limits to [0, 10] instead of default [0, 1] ax.axis([0, 10, 0, 10])ax.text(3, 8, 'boxed italics text in data coords', style='italic',bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15) font1 = {'family': 'Times New Roman','color': 'purple','weight': 'normal','size': 10,} ax.text(3, 2, 'unicode: Institut für Festk?rperphysik',fontdict=font1) ax.text(0.95, 0.01, 'colored text in axes coords',verticalalignment='bottom', horizontalalignment='right', #右下角的坐標在圖的0.95倍x軸長度、0.01倍y軸長度處transform=ax.transAxes,color='green', fontsize=15)ax.plot([2], [1], 'o') ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),arrowprops=dict(facecolor='black', shrink=0.05))plt.show()

1.8 數(shù)學表達式

使用latex語法即可。

2 tick上的text

#一般繪圖時會自動創(chuàng)建刻度，而如果通過上面的例子使用set_ticks創(chuàng)建刻度可能會導致tick的范圍與所繪制圖形的范圍不一致的問題。 #所以在下面的案例中，axs[1]中set_xtick的設(shè)置要與數(shù)據(jù)范圍所對應(yīng)，然后再通過set_xticklabels設(shè)置刻度所對應(yīng)的標簽 import numpy as np import matplotlib.pyplot as plt fig, axs = plt.subplots(2, 1, figsize=(6, 4), tight_layout=True) x1 = np.linspace(0.0, 6.0, 100) y1 = np.cos(2 * np.pi * x1) * np.exp(-x1) axs[0].plot(x1, y1) axs[0].set_xticks([0,1,2,3,4,5,6])axs[1].plot(x1, y1) axs[1].set_xticks([0,1,2,3,4,5,6])#要將x軸的刻度放在數(shù)據(jù)范圍中的哪些位置 axs[1].set_xticklabels(['zero','one', 'two', 'three', 'four', 'five','six'],#設(shè)置刻度對應(yīng)的標簽rotation=30, fontsize='small')#rotation選項設(shè)定x刻度標簽傾斜30度。 axs[1].xaxis.set_ticks_position('bottom')#set_ticks_position()方法是用來設(shè)置刻度所在的位置，常用的參數(shù)有bottom、top、both、none print(axs[1].xaxis.get_ticklines()) plt.show()

2.1 Tick Locators and Formatters

• 設(shè)置標簽的位置

Axis.set_major_locator
Axis.set_minor_locator

• 設(shè)置標簽的格式

Axis.set_major_formatter
Axis.set_minor_formatter

2.1.1 Formatters

接收字符串

import matplotlib# 接收字符串格式的例子 x1 = np.linspace(0.0, 6.0, 100) y1 = np.cos(2 * np.pi * x1) * np.exp(-x1) fig, axs = plt.subplots(2, 2, figsize=(8, 5), tight_layout=True) for n, ax in enumerate(axs.flat):ax.plot(x1*10., y1)formatter = matplotlib.ticker.FormatStrFormatter('%1.1f') axs[0, 1].xaxis.set_major_formatter(formatter)formatter = matplotlib.ticker.FormatStrFormatter('-%1.1f') axs[1, 0].xaxis.set_major_formatter(formatter)formatter = matplotlib.ticker.FormatStrFormatter('%1.5f') axs[1, 1].xaxis.set_major_formatter(formatter)plt.show()

2. 接收函數(shù)

def formatoddticks(x, pos):"""Format odd tick positions."""if x % 2:return f'{x:1.2f}'else:return ''fig, ax = plt.subplots(figsize=(5, 3), tight_layout=True) ax.plot(x1, y1) ax.xaxis.set_major_formatter(formatoddticks) plt.show()

2.1.2 Locator

使用plt或者matplotlib.ticker獲得各種locator，然后通過axs.xaxis.set_major_locator(locator)繪制：

• locator=plt.MaxNLocator(nbins=7)
• llocator=plt.FixedLocator(locs=[0,0.5,1.5,2.5,3.5,4.5,5.5,6])#直接指定刻度所在的位置
• llocator=plt.AutoLocator()#自動分配刻度值的位置
• llocator=plt.IndexLocator(offset=0.5, base=1)#面元間距是1，從0.5開始
• llocator=plt.MultipleLocator(1.5)#將刻度的標簽設(shè)置為1.5的倍數(shù)
• llocator=plt.LinearLocator(numticks=5)#線性劃分5等分，4個刻度

# 接收各種locator的例子 fig, axs = plt.subplots(2, 2, figsize=(8, 5), tight_layout=True) for n, ax in enumerate(axs.flat):ax.plot(x1*10., y1)locator = matplotlib.ticker.AutoLocator() axs[0, 0].xaxis.set_major_locator(locator)locator = matplotlib.ticker.MaxNLocator(nbins=10) axs[0, 1].xaxis.set_major_locator(locator)locator = matplotlib.ticker.MultipleLocator(5) axs[1, 0].xaxis.set_major_locator(locator)locator = matplotlib.ticker.FixedLocator([0,7,14,21,28]) axs[1, 1].xaxis.set_major_locator(locator)plt.show()

2.1.3 調(diào)整x、y軸的位置

#這個案例中展示了如何進行坐標軸的移動，如何更改刻度值的樣式 import matplotlib.pyplot as plt import numpy as np x = np.linspace(-3,3,50) y1 = 2*x+1 y2 = x**2 plt.figure() plt.plot(x,y2) plt.plot(x,y1,color='red',linewidth=1.0,linestyle = '--') plt.xlim((-3,5)) plt.ylim((-3,5)) plt.xlabel('x') plt.ylabel('y') new_ticks1 = np.linspace(-3,5,5) plt.xticks(new_ticks1) plt.yticks([-2,0,2,5],[r'$one\ shu$',r'$\alpha$',r'$three$',r'four']) ''' 上一行代碼是將y軸上的小標改成文字,其中，空格需要增加\，即'\ ',$可將格式更改成數(shù)字模式，如果需要輸入數(shù)學形式的α，則需要用\轉(zhuǎn)換，即\alpha 如果使用面向?qū)ο蟮拿钸M行畫圖，那么下面兩行代碼可以實現(xiàn)與 plt.yticks([-2,0,2,5],[r'$one\ shu$',r'$\alpha$',r'$three$',r'four']) 同樣的功能 axs.set_yticks([-2,0,2,5]) axs.set_yticklabels([r'$one\ shu$',r'$\alpha$',r'$three$',r'four']) ''' ax = plt.gca()#gca = 'get current axes' 獲取現(xiàn)在的軸 ''' ax = plt.gca()是獲取當前的axes，其中g(shù)ca代表的是get current axes。 fig=plt.gcf是獲取當前的figure，其中g(shù)cf代表的是get current figure。許多函數(shù)都是對當前的Figure或Axes對象進行處理， 例如plt.plot()實際上會通過plt.gca()獲得當前的Axes對象ax，然后再調(diào)用ax.plot()方法實現(xiàn)真正的繪圖。而在本例中則可以通過ax.spines方法獲得當前頂部和右邊的軸并將其顏色設(shè)置為不可見 然后將左邊軸和底部的軸所在的位置重新設(shè)置 最后再通過set_ticks_position方法設(shè)置ticks在x軸或y軸的位置，本示例中因所設(shè)置的bottom和left是ticks在x軸或y軸的默認值，所以這兩行的代碼也可以不寫 ''' ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.spines['left'].set_position(('data',0)) # data 0:將底部設(shè)置在數(shù)據(jù)的y=0處 ax.spines['bottom'].set_position(('data',0))# data 0:將左部設(shè)置在數(shù)據(jù)的x=0處 ax.xaxis.set_ticks_position('bottom') #設(shè)置ticks在x軸的位置 ax.yaxis.set_ticks_position('left') #設(shè)置ticks在y軸的位置 plt.show()

3 Legend

常用的幾個參數(shù)：

(1)設(shè)置圖列位置

plt.legend(loc=‘upper center’) 等同于plt.legend(loc=9)

0: ‘best’
1: ‘upper right’
2: ‘upper left’
3: ‘lower left’ |
4: ‘lower right’
5: ‘right’
6: ‘center left’ |
7: ‘center right’
8: ‘lower center’
9: ‘upper center’
10: ‘center’ |

(2)設(shè)置圖例字體大小

fontsize : int or float or {‘xx-small’, ‘x-small’, ‘small’, ‘medium’, ‘large’, ‘x-large’, ‘xx-large’}

(3)設(shè)置圖例邊框及背景

plt.legend(loc=‘best’,frameon=False) #去掉圖例邊框
plt.legend(loc=‘best’,edgecolor=‘blue’) #設(shè)置圖例邊框顏色
plt.legend(loc=‘best’,facecolor=‘blue’) #設(shè)置圖例背景顏色,若無邊框,參數(shù)無效

(4)設(shè)置圖例標題

legend = plt.legend([“CH”, “US”], title=‘China VS Us’)

(5)設(shè)置圖例名字及對應(yīng)關(guān)系

legend = plt.legend([p1, p2], [“CH”, “US”])

line_up, = plt.plot([1, 2, 3], label='Line 2') line_down, = plt.plot([3, 2, 1], label='Line 1') plt.legend([line_up, line_down], ['Line Up', 'Line Down'],loc=5, title='line',frameon=False)#loc參數(shù)設(shè)置圖例所在的位置，title設(shè)置圖例的標題，frameon參數(shù)將圖例邊框給去掉

添加多個lengend，plt.gca().add_artist()：

#這個案例是顯示多圖例legend import matplotlib.pyplot as plt import numpy as np x = np.random.uniform(-1, 1, 4) y = np.random.uniform(-1, 1, 4) p1, = plt.plot([1,2,3]) p2, = plt.plot([3,2,1]) l1 = plt.legend([p2, p1], ["line 2", "line 1"], loc='upper left')p3 = plt.scatter(x[0:2], y[0:2], marker = 'D', color='r') p4 = plt.scatter(x[2:], y[2:], marker = 'D', color='g') # 下面這行代碼由于添加了新的legend，所以會將l1從legend中給移除 plt.legend([p3, p4], ['label', 'label1'], loc='lower right', scatterpoints=1) # 為了保留之前的l1這個legend，所以必須要通過plt.gca()獲得當前的axes，然后將l1作為單獨的artist plt.gca().add_artist(l1)

作業(yè)

嘗試在一張圖中運用所講過的功能，對title、text、xlable、ylabel、數(shù)學表達式、tick and ticklabel、legend進行詳細的設(shè)計.

# Dolphins圖來源:https://matplotlib.org/gallery/shapes_and_collections/dolphin.html#sphx-glr-gallery-shapes-and-collections-dolphin-pyimport matplotlib.cm as cm import matplotlib.pyplot as plt from matplotlib.patches import Circle, PathPatch from matplotlib.path import Path from matplotlib.transforms import Affine2D import numpy as np# Fixing random state for reproducibility np.random.seed(19680801)r = np.random.rand(50) t = np.random.rand(50) * np.pi * 2.0 x = r * np.cos(t) y = r * np.sin(t)fig, ax = plt.subplots(figsize=(6, 6)) circle = Circle((0, 0), 1, facecolor='none',edgecolor=(0, 0.8, 0.8), linewidth=3, alpha=0.5) ax.add_patch(circle)im = plt.imshow(np.random.random((100, 100)),origin='lower', cmap=cm.winter,interpolation='spline36',extent=([-1, 1, -1, 1])) im.set_clip_path(circle)plt.plot(x, y, 'o', color=(0.9, 0.9, 1.0), alpha=0.8)# Dolphin from OpenClipart library by Andy Fitzsimon # <cc:License rdf:about="http://web.resource.org/cc/PublicDomain"> # <cc:permits rdf:resource="http://web.resource.org/cc/Reproduction"/> # <cc:permits rdf:resource="http://web.resource.org/cc/Distribution"/> # <cc:permits rdf:resource="http://web.resource.org/cc/DerivativeWorks"/> # </cc:License>dolphin = """ M -0.59739425,160.18173 C -0.62740401,160.18885 -0.57867129,160.11183 -0.57867129,160.11183 C -0.57867129,160.11183 -0.5438361,159.89315 -0.39514638,159.81496 C -0.24645668,159.73678 -0.18316813,159.71981 -0.18316813,159.71981 C -0.18316813,159.71981 -0.10322971,159.58124 -0.057804323,159.58725 C -0.029723983,159.58913 -0.061841603,159.60356 -0.071265813,159.62815 C -0.080250183,159.65325 -0.082918513,159.70554 -0.061841203,159.71248 C -0.040763903,159.7194 -0.0066711426,159.71091 0.077336307,159.73612 C 0.16879567,159.76377 0.28380306,159.86448 0.31516668,159.91533 C 0.3465303,159.96618 0.5011127,160.1771 0.5011127,160.1771 C 0.63668998,160.19238 0.67763022,160.31259 0.66556395,160.32668 C 0.65339985,160.34212 0.66350443,160.33642 0.64907098,160.33088 C 0.63463742,160.32533 0.61309688,160.297 0.5789627,160.29339 C 0.54348657,160.28968 0.52329693,160.27674 0.50728856,160.27737 C 0.49060916,160.27795 0.48965803,160.31565 0.46114204,160.33673 C 0.43329696,160.35786 0.4570711,160.39871 0.43309565,160.40685 C 0.4105108,160.41442 0.39416631,160.33027 0.3954995,160.2935 C 0.39683269,160.25672 0.43807996,160.21522 0.44567915,160.19734 C 0.45327833,160.17946 0.27946869,159.9424 -0.061852613,159.99845 C -0.083965233,160.0427 -0.26176109,160.06683 -0.26176109,160.06683 C -0.30127962,160.07028 -0.21167141,160.09731 -0.24649368,160.1011 C -0.32642366,160.11569 -0.34521187,160.06895 -0.40622293,160.0819 C -0.467234,160.09485 -0.56738444,160.17461 -0.59739425,160.18173 """vertices = [] codes = [] parts = dolphin.split() i = 0 code_map = {'M': Path.MOVETO,'C': Path.CURVE4,'L': Path.LINETO, }while i < len(parts):path_code = code_map[parts[i]]npoints = Path.NUM_VERTICES_FOR_CODE[path_code]codes.extend([path_code] * npoints)vertices.extend([[*map(float, y.split(','))]for y in parts[i + 1:][:npoints]])i += npoints + 1 vertices = np.array(vertices) vertices[:, 1] -= 160dolphin_path = Path(vertices, codes) dolphin_patch = PathPatch(dolphin_path, facecolor=(0.6, 0.6, 0.6),edgecolor=(0.0, 0.0, 0.0)) ax.add_patch(dolphin_patch)vertices = Affine2D().rotate_deg(60).transform(vertices) dolphin_path2 = Path(vertices, codes) dolphin_patch2 = PathPatch(dolphin_path2, facecolor=(0.5, 0.5, 0.5),edgecolor=(0.0, 0.0, 0.0)) ax.add_patch(dolphin_patch2)""" 下面是作業(yè)代碼 """ font = {'family': 'serif','color': 'blue','weight': 'bold','size': 14,} ax.set_title("Two Dolphins!",fontdict=font,loc='center') ax.text(-0.85,0.9,r'$1+1=2$',color='purple') ax.set_xlabel('x') ax.set_ylabel('y',rotation=0.5) ax.set_xticks([-1,-0.5,0,0.5,1]) ax.set_xticklabels(['I','?','Datawhale','!',"!"]) ax.plot(1,1,'blue') ax.annotate('a point',xy=(1,1),xytext=(-0.85,-0.85),arrowprops=dict(facecolor='red',width=0.5)) ax.legend('text',loc='lower right',fontsize='small')plt.show()

總結(jié)

以上是生活随笔為你收集整理的数据可视化组队学习：《Task04 - 文字图例尽眉目》笔记的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯，歡迎將生活随笔推薦給好友。