1, save the image.
fig.savefig
First, create a canvas
1. Create a canvas and axes
In the Matplotlib, plt.figure class can be seen as a container capable of accommodating a variety of axes, graphics, text and labels. plt.Axes class is a scale and with a rectangular label will eventually contain all the visual graphic elements.
Here, fig representing a legend, ax represents a coordinate axis or axes Examples instance a set of coordinates.
%matplotlib inline import matplotlib.pyplot as plt fig = plt.figure() ax = plt.axes() x = np.linspace(0,10,1000) ax.plot (x, np.sin (x))
2, the object-oriented style interfaces
# Object-oriented style # create graphical network # AX is an array comprising two Axes objects, i.e., two axes Fig, AX = plt.subplots (2 ) # Invoked on each subject Axes plot () method, are plotted sin () and COS () AX [0] .plot (X, np.sin (X)) Ax [ 1] .plot (x, np.cos (x))
3, Matlab style interfaces
plt.figure () # create graphics # Matlib Interface Style # Create a first two sub-graphs, the axis is provided, is equal to Fig, AX = plt.subplot () plt.subplot (2,. 1,. 1 ) plt.plot (x, np.sin (x)) # Create a first two sub-graphs, axes provided plt.subplot (2,. 1, 2 ) plt.plot (x, np.cos (x))
Second, the adjustment axes and lines
1, adjust the line color and style
Line style:
'-' solid line style
'--' dashed line style
'-.' dash-dot line style
':' dotted line style
colour:
'.' 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
plt.axhline (y = 1, ls = '.', c-'yellow ') # to increase the horizontal
plt.axvline (x = 1, ls = '-', c = 'red') # increase the vertical line
2, adjusts the axes
(1) adjusting the lower limit of the coordinate axes:
plt.xlim () # equivalent ax.set_xlim ()
plt.ylim () # equivalent ax.set_ylim ()
plt.axis([xmin, xmax, ymin, ymax])
(2) setting tab pattern
plt.title () # set the graphic title, equivalent to ax.set_title ()
plt.xlabel (), plt.ylabel () # Set X, Y axis title, equivalent to ax.set_xlabel (), ax.set_ylabel ()
(3) Configuration Legend
plt.legend () # Create Legend
ax.legend(frameon=False, loc='epper left')
# Selection element displayed legend # manner a plt.legend (Lines [: 2], [ ' First ' , ' SECOND ' ]) # Second way plt.plot (X, Y [:, 0], label = ' Frist ' ) plt.plot(x, y[:,1], label='second') plt.plot(x,y[:,2:]) plt.legend (gramealpha = 1, frameon = True) # default will ignore those elements without labels
Third, and more sub-graph
1, FIG. FIG.
plt.axes ([bottom, left, width, height] # [bottom coordinates, sit coordinates, width, height]
#xample1 ax1 = plt.axes() ax2 = plt.axes([0.65, 0.65, 0.2, 0.2]) #example2 fig = plt.figure() ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4], xticklabels=[], ylim=(-1.2, 1.2)) ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4], death = (1.2, 1.2 )) x = np.linspace(0, 10) ax1.plot (np.sin (x)) ax2.plot(np.cos(x))
2, a simple grid subgraph
plt.subplot (the number of rows, columns, an index value)
I in Range for (l, 7):
plt.subplot (2,3, I)
plt.text (0.5, 0.5, STR ((2,3, I)),
fontsizt = 18 is, HA = 'Center')
Fig = plt.figure () # plt.subplot_adjust can adjust the spacing between sub FIG fig.subplots_adjust (hspace = 0.4, wspace = 0.4 ) for I in Range (l, 7 ): ax=fig.add_subplot(2, 3, i)
ax.text(0.5, 0.5, str((2,3,i)),
fontstze=18, ha='center')
Equivalent to
plt.subplots(2, 3, sharex='col', sharey='row')
#比较subplot & subplots
#subplots_addjust
Fourth, text and notes
ax.text (): text notes
ax.transData: x-axis and y-axis coordinate as the data tag ax.transAxes: lower left corner as the origin of the coordinate axes, the coordinate axis in proportion to the size of the rendering coordinates. fig.transFigure: graphic to the lower left corner as the origin, according to the proportion of the size of the graphics rendering coordinates. fig, ax = plt.subplots(facecolor='lightgray') ax.axis([0, 10, 0, 10]) ax.text(1, 5, ".data:(1,5)", transform=ax.transData) ax.text(0.5, 0.2, ".Axes:(0.5, 0.2)", transform=ax.transAxes) ax.text(0.5, 0.2, ".Figure:(0.5, 0.2)", transform=fig.transFigure)
plt.annotate (): Creating arrow
Reference: "Python Data Science Handbook"