Data visualization: plotting sine and cosine curves with Matplotlib

#1： Methods and parameters used 
# linspace(start, stop, num=50, endpoint=True, 
# retstep=False, dtype=None)
#  Description of relevant parameters 
#  Specified in the start To stop Mean value 
# start: Do not omit 
# stop: Sometimes it includes, sometimes it doesn't , according to endpoint Choose , Default includes 
# num: Specify the number of equal points , The default is 50
# endpoint: Boolean value , Optional , The default is True. contain stop Just True, If it doesn't include # False
# retstep :  Boolean value , Optional , The default is False. If True, Return value and step size 
# dtype :  Output data type , Optional . If you don't specify , According to the data type of the previous parameter 
# 2：np.plot(x,y,color,lw,label,ms,mk,mec,mfc)
#  among X By the coordinates of all input points x The value of ,Y It's by and by X It contains x Corresponding y Group 
#  Into a vector 
# color  Specifies the color of the line 
# lw Specify the width of the line 
# lw Specifies the style of the line , The default is to implement 
# mk You can specify a data identifier 
# ms You can specify the size of the identifier 
# mec You can specify the boundary color of the identifier 
# mfc The internal fill color of the identifier 

import matplotlib.pyplot as plt
import numpy as np
# For normal display of Chinese labels 
plt.rcParams['font.sans-serif']=['SimHei']
#  Used to display symbols normally 
plt.rcParams['axes.unicode_minus']=False
x = np.linspace(-np.pi,np.pi,256,endpoint = 256)
sin,cos = np.sin(x),np.cos(x)
# draw , And set the relevant parameters , The label will not be displayed here , Because there is not yet 
# Add legend , Look down 
plt.plot(x,sin,color = 'blue',lw=2.5,label = ' sine sin',mec='red')
plt.plot(x,cos,color = 'red',lw = 2.5,label = ' cosine cos()')
plt.show() # Show 

# Methods and parameters used 
# plt.xlim(xmin, xmax)
# xmin：x The minimum value on the axis 
# xmax：x The maximum value on the axis 
#plt.ylim()  The same reason as above 
# How to use it , You can see the following example code 

plt.xlim(x.min()*1.5,x.max()*1.5) # take x Axis stretch 1.5 times 
plt.ylim(cos.min()*1.5,cos.max()*1.5) #  take y Axis stretch 1.5 times 
plt.show()

# The methods and parameters used 
# plt.xticks([],[])
# plt.yticks([],[])
# plt.title('',color,color,..) # Set title , The relevant parameters inside can #  Appoint 
# plt.text( Remarks x Axis coordinates , Remarks y Axis coordinates  ,' Remarks ',fontsize,color,..) # Add notes to the bottom right corner 

# What I want to say is that there are even two parameters , Show them in the form of a list .
 Now we only need to introduce the method used to set the coordinate scale . The second parameter list 
 Is used to escape . Examples are as follows .

plt.xticks([-np.pi,-np.pi/2,0,np.pi/2,np.pi],
[r'$-\pi$',r'$-\pi/2$',r'$0$',r'$\pi/2$',r'$\pi$'])
plt.yticks([-1,0,1])
plt.title(" Plot sine and cosine function curve ",fontsize = 16,color ='green')
# Add a note label to the bottom right corner of the picture 
plt.text(+2.2,-1.4,"by:jgdabc",fontsize=16,color = 'purple')
plt.show()

  Methods and parameters used ：
  plt.gca()# This method has something .
   I want to simply understand ,Python The library is too complicated . There is a big nod .
   plt.gca(), You can get axes object 
   What is axes object ？
  stay matplotlib in , The whole chart is one figure object . Every figure
  An object can contain one or more axes, and axes For the axis . Every axes
  An object is a drawing area with its own coordinate system . We can understand it as through 
  In this way, we can get axes object , And this object can help us 
  Convenient operation coordinate axis ,ok. See examples for specific operations ！

# I believe those who can understand English can understand without reading the notes 
ax = plt.gca() # obtain Axes object 
ax.spines['right'].set_color('none') # Hide right border 
ax.spines['top'].set_color('none')# Hide the upper boundary 
ax.xaxis.set_ticks_position('bottom') #x The axis coordinate scale is set below the coordinates 
ax.spines['bottom'].set_position(('data',0))# take x The axis translation passes through (0,0) The location of 
ax.yaxis.set_ticks_position('left')# take y The axis coordinate scale is set to the left of the coordinate axis 
ax.spines['left'].set_position(('data',0))# take y Axis translation to (0,0) Location 
plt.show()

 Methods and parameters used ：
plt.legend（）
 Add legend 
 This will put me above label Show the content of .

plt.legend(loc ='upper left',fontsize=12)
plt.show()

 Methods and parameters used 
plt.plot() #  This has been explained earlier , I won't repeat , Here we are 
 To add a parameter linewidth Appoint , Change it to a dotted line 
plt.scatter() # Used to draw the position of two points 
plt.annotate # Used to add annotation text , The specific explanation is explained in the example code 

```javascript
t1 = 2*np.pi/3 # Take a x Axis coordinate point 
t2 = -np.pi #  Take the second coordinate point 
# Draw lines according to , The first list is x Axis coordinate value , The second list is y Axis coordinate value 
plt.plot([t1,t1],[0,np.sin(t1)],color = 'b',linewidth = 1.5,linestyle = '--')
# Draw line 
plt.plot([t2,t2],[0,np.cos(t2)],color ='r',linewidth=1.5,linestyle="--")
# Mark the position of the two points ( How to draw a scatter diagram )
plt.scatter([t1,],[np.sin(t1),],50,color = 'b') #50 For the specified size 
# Add notes to the chart 
plt.scatter([t2,],[np.cos(t2),],50,color = 'r')
plt.annotate( r'$\sin(\frac{2\pi}{3}=\frac{\sqrt{3}}{2}$)',
              xy = (t1,np.sin(t1)), # Location of points 
              xycoords = 'data', # The offset of the annotation text 
              xytext = (+10,+30), # The horizontal and vertical distance of the text from the point 
              textcoords = 'offset points',
              fontsize =14,# The size of the comment 
              arrowprops = dict(arrowstyle = '->',connectionstyle = 'arc3,rad=.2')# The curvature pointed by the arrow 


)
plt.annotate(r'$\cos(-\pi)=-1$',
             xy = (t2,np.cos(t2)),
             xycoords = 'data', # The offset of the annotation text 
             xytext = (0 ,-40), #  The horizontal and vertical distance of the text from the point 
             textcoords = 'offset points',
             fontsize = 14,# The size of the comment 
             arrowprops = dict(arrowstyle = '->',connectionstyle='arc3,rad=.2')

             ) # Location of points 

plt.show()

# Traversal access x Axis and y Axis scale , And set the font 
for label in ax.get_xticklabels() + ax.get_yticklabels() :
    label.set_fontsize(18)
    label.set_bbox(dict(facecolor = 'r',edgecolor='g',alpha=0.5))#alpha On behalf of transparency 
# Draw the fill area 
plt.fill_between(x,np.abs(x)<0.5,sin,sin>0.5,color='g',alpha =0.8)
plt.fill_between(x,cos,where = (-2.5<x)&(x<-0.5),color = 'purple')
plt.grid() # Draw gridlines 

plt.savefig("D:\python Learn data visualization matplot Study .png",dpi = 300) Save the picture 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False
x = np.linspace(-np.pi,np.pi,256,endpoint=256)
sin,cos = np.sin(x),np.cos(x)
plt.plot(x,sin,color = 'blue',lw=2.5,label = ' sine sin',mec='red')
plt.plot(x,cos,color = 'red',lw = 2.5,label = ' cosine cos()')
plt.xlim(x.min()*1.5,x.max()*1.5)
plt.ylim(cos.min()*1.5,cos.max()*1.5)
plt.xticks([-np.pi,-np.pi/2,0,np.pi/2,np.pi],[r'$-\pi$',r'$-\pi/2$',r'$0$',r'$\pi/2$',r'$\pi$'])
plt.yticks([-1,0,1])
plt.title(" Plot sine and cosine function curve ",fontsize = 16,color ='green')
plt.text(+2.2,-1.4,"by:jgdabc",fontsize=16,color = 'purple')
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
plt.legend(loc ='upper left',fontsize=12)
t1 = 2*np.pi/3
t2 = -np.pi

plt.plot([t1,t1],[0,np.sin(t1)],color = 'b',linewidth = 1.5,linestyle = '--')

plt.plot([t2,t2],[0,np.cos(t2)],color ='r',linewidth=1.5,linestyle="--")

plt.scatter([t1,],[np.sin(t1),],50,color = 'b')

plt.scatter([t2,],[np.cos(2),],50,color = 'r')
plt.annotate( r'$\sin(\frac{2\pi}{3}=\frac{\sqrt{3}}{2}$)',
              xy = (t1,np.sin(t1)),
              xycoords = 'data',
              xytext = (+10,+30),
              textcoords = 'offset points',
              fontsize =14,
              arrowprops = dict(arrowstyle= '->',connectionstyle = 'arc3,rad=.2')# The curvature pointed by the arrow 


)
plt.annotate(r'$\cos(-\pi)=-1$',
             xy = (t2,np.cos(t2)),
             xycoords = 'data',
             xytext = (0 ,-40),
             textcoords = 'offset points',
             fontsize = 14,
             arrowprops = dict(arrowstyle = '->',connectionstyle='arc3,rad=.2')

             )
for label in ax.get_xticklabels() + ax.get_yticklabels() :
    label.set_fontsize(18)
    label.set_bbox(dict(facecolor = 'r',edgecolor='g',alpha=0.5))

plt.fill_between(x,np.abs(x)<0.5,sin,sin>0.5,color='g',alpha =0.8)
plt.fill_between(x,cos,where = (-2.5<x)&(x<-0.5),color = 'purple')
plt.grid()

plt.savefig("D:\python Learn data visualization matplot Study .png",dpi = 300)
plt.show()