Pyplot tutorial
http://matplotlib.org/1.3.1/users/pyplot_tutorial.html
환경 : windows 7 32bit, matplotlib 1.3.1, numpy 1.8.1
matplotlib.pyplot : MATLAB 처럼 matplotlib 을 사용할수있게 하는 명령 스타일 함수들(command style functions) 모음이다.
ex) figure 생성하고 -> figure에 plotting area 생성 -> plotting area 에 line 그리고 -> label 등을 꾸미고.. etc.
* plotting 함수들은 current axes 에 작용한다.
import matplotlib.pyplot as plt
plt.plot([1,2,3,4])
plt.ylabel('some numbers')
plt.show()
** plot() 에 값 한개만 입력시에는 y 값으로 생각한다.
-- x 값은 자동생성한다.(0 부터 시작함)
import matplotlib.pyplot as plt
plt.plot([1,2,3,4], [1,4,9,16], 'ro')
plt.axis([0, 6, 0, 20]) # [xmin, xmax, ymin, ymax]
plt.show()
matplotlib.pyplot.plot(*args, **kwargs)Plot lines and/or markers to the Axes.
plot(x, y) # plot x and y using default line style and color
plot(x, y, 'bo') # plot x and y using blue circle markers
plot(y) # plot y using x as index array 0..N-1
plot(y, 'r+') # ditto, but with red plusses
a.plot(x1, y1, 'g^', x2, y2, 'g-')
format string characters are accepted to control the line style or marker:
| character | description |
|---|
| '-' | solid line style |
| '--' | dashed line style |
| '-.' | dash-dot line style |
| ':' | dotted line style |
| '.' | point marker |
| ',' | pixel marker |
| 'o' | circle marker |
| 'v' | triangle_down marker |
| '^' | triangle_up marker |
| '<' | triangle_left marker |
| '>' | triangle_right marker |
| '1' | tri_down marker |
| '2' | tri_up marker |
| '3' | tri_left marker |
| '4' | tri_right marker |
| 's' | square marker |
| 'p' | pentagon marker |
| '*' | star marker |
| 'h' | hexagon1 marker |
| 'H' | hexagon2 marker |
| '+' | plus marker |
| 'x' | x marker |
| 'D' | diamond marker |
| 'd' | thin_diamond marker |
| '|' | vline marker |
| '_' | hline marker
|
| character | color |
|---|
| ‘b’ | blue |
| ‘g’ | green |
| ‘r’ | red |
| ‘c’ | cyan |
| ‘m’ | magenta |
| ‘y’ | yellow |
| ‘k’ | black |
| ‘w’ | white
|
http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.axismatplotlib.pyplot.axis(*v, **kwargs)Convenience method to get or set axis properties.
sets the min and max of the x and y axes, with v = [xmin, xmax, ymin, ymax].:
*** matplotlib 은 numpy array 를 사용한다.
: seqence 자료 (list etc)는 내부적으로 numpy array로 변환하여 사용한다.
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, 'r--', t, t**2, 'bs', t, t**3, 'g^')
plt.show()
line 속성 제어하기 (Controlling line properties)
참고 : matplotlib.lines.Line2D
http://matplotlib.org/1.3.1/api/artist_api.html#matplotlib.lines.Line2D
plt.plot(x, y, linewidth=2.0)
line, = plt.plot(x, y, '-')
line.set_antialiased(False) # turn off antialising
* setp() 명령어 사용
lines = plt.plot(x1, y1, x2, y2)
# use keyword args
plt.setp(lines, color='r', linewidth=2.0)
# or MATLAB style string value pairs
plt.setp(lines, 'color', 'r', 'linewidth', 2.0)
* line 속성 얻을때도, setp() 명령어 사용
In [69]: lines = plt.plot([1,2,3])
In [70]: plt.setp(lines)
alpha: float
animated: [True | False]
antialiased or aa: [True | False]
...snip
여러개의 figure & axes 로 작업하기 (Working with multiple figures and axes)
pyplot --- current figure, current axes 의 개념이 있다.
* 모든 plotting 명령어는 current axes 에 적용된다.
* gca() --- current axes 반환 (matplotlib.axes.Axes instance)
* gcf() --- current figure 반환 (matplotlib.figure.Figure instance)
import numpy as np
import matplotlib.pyplot as plt
def f(t):
return np.exp(-t) * np.cos(2*np.pi*t)
t1 = np.arange(0.0, 5.0, 0.1)
t2 = np.arange(0.0, 5.0, 0.02)
plt.figure(1) # 생략 가능
plt.subplot(211) # row number, column number, figure number
plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')
plt.subplot(212)
plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
plt.show()
--- plt.figure(1) 생략 가능 : defalut 로 figure(1) 생성 된다.
--- axes 지정 하지않으면, defalut 로 subplot(111) 생성됨.
*** figure() 를 여러번 호출하여, 여러개의 figure 생성 가능하다.
(각 figure 는 자신만의 axes , subplot 를 갖는다)
import matplotlib.pyplot as plt
plt.figure(1) # the first figure
plt.subplot(211) # the first subplot in the first figure
plt.plot([1,2,3])
plt.subplot(212) # the second subplot in the first figure
plt.plot([4,5,6])
plt.figure(2) # a second figure
plt.plot([4,5,6]) # creates a subplot(111) by default
plt.figure(1) # figure 1 current; subplot(212) still current
plt.subplot(211) # make subplot(211) in figure1 current
plt.title('Easy as 1,2,3') # subplot 211 title
* clf() --- clear current figure.
* cla() --- clear current axes.
*** 메모리 해제 : close()
- close() 호출되기 전까지는, pyplot 이 참조를 계속 가지고 있다.
텍스트 작업하기 (Working with text)
http://matplotlib.org/1.3.1/api/pyplot_api.html#matplotlib.pyplot.text
matplotlib.pyplot.text(x, y, s, fontdict=None, withdash=False, **kwargs)
-- 좌표 x,y 에 텍스트 s 출력하기
import numpy as np
import matplotlib.pyplot as plt
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
# the histogram of the data
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ')
plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()
t = plt.xlabel('my data', fontsize=14, color='red')
텍스트로 수학식 표현하기 (Using mathematical expressions in text)
built-in TeX expression parser and layout engine
plt.title(r'$\sigma_i=15$')
참고 : http://matplotlib.org/1.3.1/users/mathtext.html#mathtext-tutorial
텍스트 주석달기 (Annotating text)
참고 : http://matplotlib.org/1.3.1/api/pyplot_api.html#matplotlib.pyplot.annotate
matplotlib.pyplot.annotate(*args, **kwargs)
annotate(s, xy, xytext=None, xycoords='data',
textcoords='data', arrowprops=None, **kwargs)--- 주석 = s, 화살표 시작 = x,y , 주석 텍스트 시작 = xytext
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('local max', xy=(2, 1), xytext=(3, 1.5),
arrowprops=dict(facecolor='black', shrink=0.05),
)
plt.ylim(-2,2)
plt.show()
