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matplotlib

1. plt.plot(x,y)

plt.plot(x, y,format_string,**kwargs)?

x-axis data, y-axis data, format_string format string for control curve?

format_string consists of a color character, a style character, and a marker character

import matplotlib.pyplot as plt

plt.plot([1,2,3,6],[4,5,8,1],'g-s ')?

plt.show()

Results

**kwards: ?

color color?

linestyle line style?

marker Marker style?

markerfacecolor Marker color?

markersize Marker size, etc.?

plt.plot([5,4,3,2,1])

plt.show()

Results

plt.plot([20,2,40,6,80]) #Defaults x to [0,1,2,3,4,...].

plt.show()

Results

plt.plot() Parameter Settings

Property Value Type

alpha Controls transparency, with 0 being fully transparent and 1 being opaque

animated [True False]

antialiased or aa [True False]

clip_box a matplotlib.transform.Bbox instance

clip_on [True False]

clip_path a Path instance and a Transform instance, a Patch

color or c color settings

contains the hit testing function

dash_capstyle ['butt ' 'round' 'projecting']

dash_joinstyle ['miter ' 'round' 'bevel']

dashes sequence of on/off ink in points

data data ( np.array xdata, np.array ydata)

figure drawing board object a matplotlib.figure.Figure instance

label diagram

linestyle or ls line style [' -' '-' '-. ':' 'steps' ...]

linewidth or lw width float value in points

lod [True False]

marker data point settings ['+' ',' '.' '1' '2' '3' '4']

markeredgecolor or mec any matplotlib color

markeredgewidth or mew float value in points

markerfacecolor or mfc any matplotlib color

markersize or ms float

markevery [ None integer (startind, stride) ]

picker used in interactive line selection

pickradius the line pick selection radius

solid_capstyle ['butt' 'round' ' projecting']

solid_joinstyle ['miter' 'round' 'bevel ']

transform a matplotlib.transforms.Transform instance

visible [True False]

xdata np.array

ydata np. array

zorder any number

determine x, y values, print them out

x=np.linspace(-1,1,5)

y=2*x+1

plt.plot(x,y)

plt.show()

2. plt.figure() is used to draw, customize canvas size

fig1 = plt.figure(num='fig111111', figsize=(10, 3), dpi=75, facecolor='#FFFFFF', edgecolor='#0000FF')

plt.plot(x,y1) ? #Plt.plot operation after variable fig1, graph will be displayed in fig1

fig2 = plt.figure(num='fig222222', figsize=(6, 3), dpi=75, facecolor='#FFFFFF', edgecolor='# FF0000')

plt.plot(x,y2) ? #Plt.plot operation after variable fig2, graph will be displayed in fig2

plt.show()

plt.close()

Results

fig1 = plt.figure(num='fig111111', figsize=(10, 3) , dpi=75, facecolor='#FFFFFF', edgecolor='#0000FF')

plt.plot(x,y1)

plt.plot(x,y2)

fig2 = plt.figure(num='fig222222', figsize=(6, 3), dpi=75, facecolor='#FFFFFF', edgecolor='#FF0000')

plt.show()

plt.close()

Results:

3. plt.subplot(222 )

The size of the canvas will be set by figure into several parts, the parameter '221' that 2 (row) x 2 (colu), that is, the canvas is divided into 2x2, two rows and two columns of the four regions, 1 means that the choice of graphic output in the first region, the graphic output region must be in the parameters of the "line x column" range, here must be selected between 1 and 2 -- if the parameter is set to subplot (111), it means that the entire canvas output, not divided into small areas, graphics output directly on the entire canvas

plt.subplot(222)?

plt.plot(y,xx) #Output the graph in the second area of the 2x2 canvas

plt.show()

plt.subplot(223)? # Output the graph in the third area in the 2x2 canvas

plt.plot(y,xx)

plt.subplot(224)? # Output the graph in the fourth area of the 2x2 canvas

plt.plot(y,xx)

4. plt.xlim set the x-axis or y-axis scale range

such as

plt.xlim(0,1000)? #? set x-axis scale range from 0~1000 #lim is limit, range

plt.ylim(0,20) # set y-axis scale range from 0~20

5, plt.xticks(): set x-axis scale expression

fig2 = plt.figure(num=' fig222222', figsize=(6, 3), dpi=75, facecolor='#FFFFFF', edgecolor='#FF0000')

plt.plot(x,y2)

plt.xticks(np.linspace(0,1000, 15,endpoint=True))? # Set x-axis scale

plt.yticks(np.linspace(0,20,10,endpoint=True))

Results

6. ax2.set_title('xxx') set the title and draw the graph

# Generate [1,2,3,... ,9] sequence

x = np.range(1,10)

y = x

fig = plt.figure()

ax1 = fig.add_subplot(221)

#Set title

ax1.set_title(' Scatter Plot1')

plt.xlabel('M')

plt.ylabel('N')

ax2 = fig.add_subplot(222)

ax2.set_title('Scatter Plot2clf')

#Set X axis label

plt.xlabel('X') ? #Setting the X/Y axis label is done after the corresponding figure to correspond to that figure

#Setting the Y axis label

plt.ylabel('Y')

#Drawing a scatterplot

ax1.scatter(x,y,c = 'r',marker = 'o') ? #You can see that drawing a scatterplot is manipulating the figure

ax2.scatter(x,y,c = 'b',marker = 'x')

#Set the icon

plt.legend('show picture x1 ')

#Display the drawn picture

plt.show()

Results

7, plt.hist () plot histogram (can be Gaussian function of these drawings)

plotting can be called matplotlib.pyplot library to carry out, which hist function can be plotted directly on the histogram

call mode:

n, bins, patches = plt.hist(arr, bins=10, normed=0, facecolor='black', edgecolor='black',alpha=1, histtype='bar')

hist has a lot of parameters, but the common ones are these six Only the first one is required, the next four are optional

arr: the one-dimensional array from which the histogram is to be computed

bins: the number of bars in the histogram, optional, defaults to 10

normed: whether or not to normalize the resulting histogram vector. 'barstacked', 'step', 'stepfilled'

Return value :

n: histogram vector. Normalized or not is set by normed

bins: return the interval range of each bin

patches: return the data contained in each bin, it is a list

from skimage import data

import matplotlib. pyplot as plt

img=data.camera()

plt.figure("hist")

arr=img.flatten()

n, bins, patches = plt.hist(arr, bins=256, normed=1,edgecolor normed=1,edgecolor='None',facecolor='red')?

plt.show()

Example:

mu, sigma = 0, .1

s = np.random.normal(loc=mu, scale=sigma, size=1000)

a,b,c = plt.hist(s, bins= 3)

print("a: ",a)

print("b: ",b)

print("c: ",c)

plt.show()

Results:

a:? [ 85. 720. 195.] ? #Value for each column

b:? [-0.36109509 -0.1357318 0.08963149? 0.31499478] #Interval range for each column

c:? <a list of 3 Patch objects> #Total*** how many columns

8. ax1.scatter(x,y,c = 'r',marker = 'o')?

Use note: determine the figure must determine the quadrant, and then use scatter, or do not determine the quadrant, directly use plt.scatter

x = np.arange(1,10)

y = x

fig = plt.figure()

a= plt.subplot() #defaults to one quadrant

# a=fig.add_subplot(222)

a.scatter(x,y,c='r',marker='o')

plt.show()

Results

x = np. arange(1,10)

y = x

plt.scatter(x,y,c='r',marker='o')

plt.show()

Results

import numpy as np

import matplotlib.pyplot as plt

x = np.range(1,10)

y = x

plt.figure()

plt.scatter(x,y,c='r',marker='o')

plt.show ()

Results

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