鏂囨憳锛歮atplotlib缁樺浘鍙鍖栫煡璇嗙偣鏁寸悊

Posted 2021-01-26

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鍘熸枃锛歨ttp://michaelxiang.me/2016/05/14/python-matplotlib-basic/

鏃犺浣犲伐浣滃湪浠€涔堥」鐩笂锛孖Python閮芥槸鍊煎緱鎺ㄨ崘鐨勩€傚埄鐢?code>ipython --pylab锛屽彲浠ヨ繘鍏yLab妯″紡锛屽凡缁忓鍏ヤ簡matplotlib搴撲笌鐩稿叧杞欢鍖咃紙渚嬪Numpy鍜孲cipy)锛岄鍙互鐩存帴浣跨敤鐩稿叧搴撶殑鍔熻兘銆?/p>

鏈枃浣滀负瀛︿範杩囩▼涓matplotlib涓€浜涘父鐢ㄧ煡璇嗙偣鐨勬暣鐞嗭紝鏂逛究鏌ユ壘銆?/p>

杩欐牱IPython閰嶇疆涓轰娇鐢ㄤ綘鎵€鎸囧畾鐨刴atplotlib GUI鍚庣锛圱K/wxPython/PyQt/Mac OS X native/GTK)銆傚浜庡ぇ閮ㄥ垎鐢ㄦ埛鑰岃█锛岄粯璁ょ殑鍚庣灏卞凡缁忓鐢ㄤ簡銆侾ylab妯″紡杩樹細鍚慖Python寮曞叆涓€澶у爢妯″潡鍜屽嚱鏁颁互鎻愪緵涓€绉嶆洿鎺ヨ繎MATLAB鐨勭晫闈€?/p>

matplotlib-缁樺埗绮剧編鐨勫浘琛?/a>

matplotlib.pyplot.plt鍙傛暟浠嬬粛

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import matplotlib.pyplot as plt labels=鈥榝rogs鈥?鈥榟ogs鈥?鈥榙ogs鈥?鈥榣ogs鈥?br data-filtered="filtered">sizes=15,20,45,10 colors=鈥榶ellowgreen鈥?鈥榞old鈥?鈥榣ightskyblue鈥?鈥榣ightcoral鈥?br data-filtered="filtered">explode=0,0.1,0,0 plt.pie(sizes,explode=explode,labels=labels,colors=colors,autopct=鈥?1.1f%%鈥?shadow=True,startangle=50) plt.axis(鈥榚qual鈥? plt.show()

matplotlib鍥炬爣姝ｅ父鏄剧ず涓枃

涓轰簡鍦ㄥ浘琛ㄤ腑鑳藉鏄剧ず涓枃鍜岃礋鍙风瓑锛岄渶瑕佷笅闈竴娈佃缃細

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import matplotlib.pyplot as plt plt.rcParams[鈥榝ont.sas-serig鈥榏=[鈥楽imHei鈥榏 #鐢ㄦ潵姝ｅ父鏄剧ず涓枃鏍囩 plt.rcParams[鈥榓xes.unicode_minus鈥榏=False #鐢ㄦ潵姝ｅ父鏄剧ず璐熷彿

 

matplotlib inline鍜宲ylab inline

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• 浣跨敤鍙傛暟瀛楀吀(rcParams)
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閰嶇疆鏂囦欢鍖呮嫭浠ヤ笅閰嶇疆椤癸細

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figure: 鎺у埗dpi銆佽竟鐣岄鑹层€佸浘褰㈠ぇ灏忋€佸拰瀛愬尯( subplot)璁剧疆
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棰滆壊

鍙互閫氳繃璋冪敤matplotlib.pyplot.colors()寰楀埌matplotlib鏀寔鐨勬墍鏈夐鑹层€?/p>

鍒悕	棰滆壊	鍒悕	棰滆壊
b	钃濊壊	g	缁胯壊
r	绾㈣壊	y	榛勮壊
c	闈掕壊	k	榛戣壊
m	娲嬬孩鑹?/td>	w	鐧借壊

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• 浣跨敤html鍗佸叚杩涘埗瀛楃涓?nbsp;color=鈥榚eefff鈥?/code> 浣跨敤鍚堟硶鐨凥TML棰滆壊鍚嶅瓧锛堚€檙ed鈥?鈥檆hartreuse鈥欑瓑锛夈€?/li>
• 涔熷彲浠ヤ紶鍏ヤ竴涓綊涓€鍖栧埌[0,1]鐨凴GB鍏冪銆?nbsp;color=(0.3,0.3,0.4)

寰堝鏂规硶鍙互浠嬬粛棰滆壊鍙傛暟锛屽title()銆?br data-filtered="filtered">plt.tilte(鈥楾itle in a custom color鈥?color=鈥?123456鈥橈級

鍩虹

濡傛灉浣犲悜plot()鎸囦护鎻愪緵浜嗕竴缁寸殑鏁扮粍鎴栧垪琛紝閭ｄ箞matplotlib灏嗛粯璁ゅ畠鏄竴绯诲垪鐨剏鍊硷紝骞惰嚜鍔ㄤ负浣犵敓鎴恱鐨勫€笺€傞粯璁ょ殑x鍚戦噺浠?寮€濮嬪苟涓斿叿鏈夊拰y鍚屾牱鐨勯暱搴︼紝鍥犳x鐨勬暟鎹槸[0,1,2,3].

鍥剧墖鏉ヨ嚜:缁樺浘: matplotlib鏍稿績鍓栨瀽

纭畾鍧愭爣鑼冨洿

• plt.axis([xmin, xmax, ymin, ymax])
  涓婇潰渚嬪瓙閲岀殑axis()鍛戒护缁欏畾浜嗗潗鏍囪寖鍥淬€?/li>
• xlim(xmin, xmax)鍜寉lim(ymin, ymax)鏉ヨ皟鏁磝,y鍧愭爣鑼冨洿

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  %matplotlib inline import numpy as np import matplotlib.pyplot as plt from pylab import * x = np.arange(-5.0, 5.0, 0.02) y1 = np.sin(x) plt.figure(1) plt.subplot(211) plt.plot(x, y1) plt.subplot(212) #璁剧疆x杞磋寖鍥?br data-filtered="filtered">xlim(-2.5, 2.5) #璁剧疆y杞磋寖鍥?br data-filtered="filtered">ylim(-1, 1) plt.plot(x, y1)

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import numpy as np
import matplotlib.pyplot as plt

# evenly sampled time at 200ms intervals
t = np.arange(0., 5., 0.2)

# red dashes, blue squares and green triangles
plt.plot(t, t, 鈥榬--鈥? t, t**2, 鈥榖s鈥? t, t**3, 鈥榞^鈥?
plt.show()

 

plt.figure()

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import matplotlib.pyplot as plt plt.figure(1) # 绗竴寮犲浘 plt.subplot(211) # 绗竴寮犲浘涓殑绗竴寮犲瓙鍥?br data-filtered="filtered">plt.plot([1,2,3]) plt.subplot(212) # 绗竴寮犲浘涓殑绗簩寮犲瓙鍥?br data-filtered="filtered">plt.plot([4,5,6]) plt.figure(2) # 绗簩寮犲浘 plt.plot([4,5,6]) # 榛樿鍒涘缓瀛愬浘subplot(111) plt.figure(1) # 鍒囨崲鍒癴igure 1 ; 瀛愬浘subplot(212)浠嶆棫鏄綋鍓嶅浘 plt.subplot(211) # 浠ゅ瓙鍥緎ubplot(211)鎴愪负figure1鐨勫綋鍓嶅浘 plt.title(鈥楨asy as 1,2,3鈥? # 娣诲姞subplot 211 鐨勬爣棰?br data-filtered="filtered">

 

figure鎰熻灏辨槸缁欏浘鍍廔D锛屼箣鍚庡彲浠ョ储寮曞畾浣嶅埌瀹冦€?/p>

plt.text()娣诲姞鏂囧瓧璇存槑

• text()鍙互鍦ㄥ浘涓殑浠绘剰浣嶇疆娣诲姞鏂囧瓧锛屽苟鏀寔LaTex璇硶
• xlable(), ylable()鐢ㄤ簬娣诲姞x杞村拰y杞存爣绛?/li>
• title()鐢ㄤ簬娣诲姞鍥剧殑棰樼洰

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import numpy as np import matplotlib.pyplot as plt mu, sigma = 100, 15 x = mu + sigma * np.random.randn(10000) # 鏁版嵁鐨勭洿鏂瑰浘 n, bins, patches = plt.hist(x, 50, normed=1, facecolor=鈥榞鈥? alpha=0.75) plt.xlabel(鈥楽marts鈥? plt.ylabel(鈥楶robability鈥? #娣诲姞鏍囬 plt.title(鈥楬istogram of IQ鈥? #娣诲姞鏂囧瓧 plt.text(60, .025, r鈥?mu=100, sigma=15$鈥? plt.axis([40, 160, 0, 0.03]) plt.grid(True) plt.show()

text涓墠涓や釜鍙傛暟鎰熻搴旇鏄枃鏈嚭鐜扮殑鍧愭爣浣嶇疆銆?/p>

plt.annotate()鏂囨湰娉ㄩ噴

鍦ㄦ暟鎹彲瑙嗗寲鐨勮繃绋嬩腑锛屽浘鐗囦腑鐨勬枃瀛楃粡甯歌鐢ㄦ潵娉ㄩ噴鍥句腑鐨勪竴浜涚壒寰併€備娇鐢╝nnotate()鏂规硶鍙互寰堟柟渚垮湴娣诲姞姝ょ被娉ㄩ噴銆傚湪浣跨敤annotate鏃讹紝瑕佽€冭檻涓や釜鐐圭殑鍧愭爣锛氳娉ㄩ噴鐨勫湴鏂箈y(x, y)鍜屾彃鍏ユ枃鏈殑鍦版柟xytext(x, y)銆俒^1]

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import numpy as np import matplotlib.pyplot as plt ax = plt.subplot(111) t = np.arange(0.0, 5.0, 0.01) s = np.cos(2*np.pi*t) line, = plt.plot(t, s, lw=2) plt.annotate(鈥榣ocal max鈥? xy=(2, 1), xytext=(3, 1.5), arrowprops=dict(facecolor=鈥榖lack鈥? shrink=0.05), ) plt.ylim(-2,2) plt.show()

[^1]:DataHub-Python 鏁版嵁鍙鍖栧叆闂?

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# 瀵煎叆 matplotlib 鐨勬墍鏈夊唴瀹癸紙nympy 鍙互鐢?np 杩欎釜鍚嶅瓧鏉ヤ娇鐢級
from pylab import *

# 鍒涘缓涓€涓?8 * 6 鐐癸紙point锛夌殑鍥撅紝骞惰缃垎杈ㄧ巼涓?80
figure(figsize=(8,6), dpi=80)

# 鍒涘缓涓€涓柊鐨?1 * 1 鐨勫瓙鍥撅紝鎺ヤ笅鏉ョ殑鍥炬牱缁樺埗鍦ㄥ叾涓殑绗?1 鍧楋紙涔熸槸鍞竴鐨勪竴鍧楋級
subplot(1,1,1)

X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
C,S = np.cos(X), np.sin(X)

# 缁樺埗浣欏鸡鏇茬嚎锛屼娇鐢ㄨ摑鑹茬殑銆佽繛缁殑銆佸搴︿负 1 锛堝儚绱狅級鐨勭嚎鏉?br data-filtered="filtered">plot(X, C, color="blue", linewidth=1.0, linestyle="-")

# 缁樺埗姝ｅ鸡鏇茬嚎锛屼娇鐢ㄧ豢鑹茬殑銆佽繛缁殑銆佸搴︿负 1 锛堝儚绱狅級鐨勭嚎鏉?br data-filtered="filtered">plot(X, S, color="r", lw=4.0, linestyle="-")

plt.axis([-4,4,-1.2,1.2])
# 璁剧疆杞磋鍙?br data-filtered="filtered">
xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
       [r鈥?-pi$鈥? r鈥?-pi/2$鈥? r鈥?0$鈥? r鈥?+pi/2$鈥? r鈥?+pi$鈥榏)

yticks([-1, 0, +1],
       [r鈥?-1$鈥? r鈥?0$鈥? r鈥?+1$鈥榏)
# 鍦ㄥ睆骞曚笂鏄剧ず
show()

褰撴垜浠缃鍙风殑鏃跺€欙紝鎴戜滑鍙互鍚屾椂璁剧疆璁板彿鐨勬爣绛俱€傛敞鎰忚繖閲屼娇鐢ㄤ簡 LaTeX銆俒^2]

[^2]:Matplotlib 鏁欑▼

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ax = gca()
ax.spines[鈥榬ight鈥榏.set_color(鈥榥one鈥?
ax.spines[鈥榯op鈥榏.set_color(鈥榥one鈥?
ax.xaxis.set_ticks_position(鈥榖ottom鈥?
ax.spines[鈥榖ottom鈥榏.set_position((鈥榙ata鈥?0))
ax.yaxis.set_ticks_position(鈥榣eft鈥?
ax.spines[鈥榣eft鈥榏.set_position((鈥榙ata鈥?0))

杩欎釜鍦版柟纭疄娌＄湅鎳傦紝鍥э紝浠ュ悗鍐嶈鍚э紝鎰熻灏辨槸绉诲姩浜嗗潗鏍囪酱鐨勪綅缃€?/p>

plt.legend()娣诲姞鍥句緥

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plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine") plot(X, S, color="red", linewidth=2.5, linestyle="-", label="sine") legend(loc=鈥榰pper left鈥?

matplotlib.pyplot

浣跨敤plt.style.use(鈥榞gplot鈥?鍛戒护锛屽彲浠ヤ綔鍑篻gplot椋庢牸鐨勫浘鐗囥€?/p>

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# Import necessary packages import pandas as pd %matplotlib inline import matplotlib.pyplot as plt plt.style.use(鈥榞gplot鈥? from sklearn import datasets from sklearn import linear_model import numpy as np # Load data boston = datasets.load_boston() yb = boston.target.reshape(-1, 1) Xb = boston[鈥榙ata鈥榏[:,5].reshape(-1, 1) # Plot data plt.scatter(Xb,yb) plt.ylabel(鈥榲alue of house /1000 ($)鈥? plt.xlabel(鈥榥umber of rooms鈥? # Create linear regression object regr = linear_model.LinearRegression() # Train the model using the training sets regr.fit( Xb, yb) # Plot outputs plt.scatter(Xb, yb, color=鈥榖lack鈥? plt.plot(Xb, regr.predict(Xb), color=鈥榖lue鈥? linewidth=3) plt.show()

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t = 2*np.pi/3
# 浣滀竴鏉″瀭鐩翠簬x杞寸殑绾挎锛岀敱鏁板鐭ヨ瘑鍙煡锛屾í鍧愭爣涓€鑷寸殑涓や釜鐐瑰氨鍦ㄥ瀭鐩翠簬鍧愭爣杞寸殑鐩寸嚎涓婁簡銆傝繖涓や釜鐐规槸璧峰鐐广€?br data-filtered="filtered">plot([t,t],[0,np.cos(t)], color =鈥榖lue鈥? linewidth=2.5, linestyle="--")
scatter([t,],[np.cos(t),], 50, color =鈥榖lue鈥?

annotate(r鈥?sin(frac{2pi}{3})=frac{sqrt{3}}{2}$鈥?
         xy=(t, np.sin(t)), xycoords=鈥榙ata鈥?
         xytext=(+10, +30), textcoords=鈥榦ffset points鈥? fontsize=16,
         arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plot([t,t],[0,np.sin(t)], color =鈥榬ed鈥? linewidth=2.5, linestyle="--")
scatter([t,],[np.sin(t),], 50, color =鈥榬ed鈥?

annotate(r鈥?cos(frac{2pi}{3})=-frac{1}{2}$鈥?
         xy=(t, np.cos(t)), xycoords=鈥榙ata鈥?
         xytext=(-90, -50), textcoords=鈥榦ffset points鈥? fontsize=16,
         arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

 

plt.subplot()

plt.subplot(2,3,1)琛ㄧず鎶婂浘鏍囧垎鍓叉垚2*3鐨勭綉鏍笺€備篃鍙互绠€鍐?code>plt.subplot(231)銆傚叾涓紝绗竴涓弬鏁版槸琛屾暟锛岀浜屼釜鍙傛暟鏄垪鏁帮紝绗笁涓弬鏁拌〃绀哄浘褰㈢殑鏍囧彿銆?/p>

plt.axes()

鎴戜滑鍏堟潵鐪嬩粈涔堟槸Figure鍜孉xes瀵硅薄銆傚湪matplotlib涓紝鏁翠釜鍥惧儚涓轰竴涓狥igure瀵硅薄銆傚湪Figure瀵硅薄涓彲浠ュ寘鍚竴涓紝鎴栬€呭涓狝xes瀵硅薄銆傛瘡涓狝xes瀵硅薄閮芥槸涓€涓嫢鏈夎嚜宸卞潗鏍囩郴缁熺殑缁樺浘鍖哄煙銆傚叾閫昏緫鍏崇郴濡備笅^3锛?br data-filtered="filtered">

plt.axes-瀹樻柟鏂囨。

• axes() by itself creates a default full subplot(111) window axis.
• axes(rect, axisbg=鈥檞鈥? where rect = [left, bottom, width, height] in normalized (0, 1) units. axisbg is the background color for the axis, default white.
• axes(h) where h is an axes instance makes h the current axis. An Axes instance is returned.

  rect=[宸? 涓? 瀹? 楂榏 瑙勫畾鐨勭煩褰㈠尯鍩?rect鐭╁舰绠€鍐欙紝杩欓噷鐨勬暟鍊奸兘鏄互figure澶у皬涓烘瘮渚嬶紝鍥犳锛岃嫢鏄涓や釜axes骞舵帓鏄剧ず锛岄偅涔坅xes[2]鐨勫乏=axes[1].宸?axes[1].瀹斤紝杩欐牱axes[2]鎵嶄笉浼氬拰axes[1]閲嶅彔銆?/p>

show code:

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http://matplotlib.org/examples/pylab_examples/axes_demo.html import matplotlib.pyplot as plt import numpy as np # create some data to use for the plot dt = 0.001 t = np.arange(0.0, 10.0, dt) r = np.exp(-t[:1000]/0.05) # impulse response x = np.random.randn(len(t)) s = np.convolve(x, r)[:len(x)]*dt # colored noise # the main axes is subplot(111) by default plt.plot(t, s) plt.axis([0, 1, 1.1*np.amin(s), 2*np.amax(s)]) plt.xlabel(鈥榯ime (s)鈥? plt.ylabel(鈥榗urrent (nA)鈥? plt.title(鈥楪aussian colored noise鈥? # this is an inset axes over the main axes a = plt.axes([.65, .6, .2, .2], axisbg=鈥榶鈥? n, bins, patches = plt.hist(s, 400, normed=1) plt.title(鈥楶robability鈥? plt.xticks([]) plt.yticks([]) # this is another inset axes over the main axes a = plt.axes([0.2, 0.6, .2, .2], axisbg=鈥榶鈥? plt.plot(t[:len(r)], r) plt.title(鈥業mpulse response鈥? plt.xlim(0, 0.2) plt.xticks([]) plt.yticks([]) plt.show()

 

[^3]:缁樺浘: matplotlib鏍稿績鍓栨瀽

pyplot.pie鍙傛暟

• matplotlib.pyplot.pie

colors棰滆壊

鎵惧嚭matpltlib.pyplot.plot涓殑colors鍙互鍙栧摢浜涘€硷紵

• so-Named colors in matplotlib
• CSDN-matplotlib瀛︿範涔嬶紙鍥涳級璁剧疆绾挎潯棰滆壊銆佸舰鐘?/a>

  1 2	for name,hex in matplotlib.colors.cnames.iteritems(): print name,hex

鎵撳嵃棰滆壊鍊煎拰瀵瑰簲鐨凴GB鍊笺€?/p>

• plt.axis(鈥榚qual鈥?閬垮厤姣斾緥鍘嬬缉涓烘き鍦?/li>

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