Matplotlib attribute and annotation

There are two main ways to decorate most icons ： Use procedural pyplot Interface （ namely matplotlib.pyplot） And more object-oriented native matplotlibAPI.
matplotlib The main thing is to understand figure( canvas )、axes( Coordinate system )、axis( Axis ) The relationship between the three .

remove set_axis_off,set_axis_on,set_axisbelow, Get rid of the rest set_ Prefix can be used as plt.subplot Parameters of .

plt.subplot(211, sharex=ax1, sharey=ax1, label='label',alpha=0.5, fc='r', yscale='log')

Axes Drawing method

Method	explain
Axes.plot	take y Yes x Draw as a line or mark .
Axes.errorbar	take y And x Draw as lines and with error bars / Or mark .
Axes.scatter	y And y The scatter diagram of
Axes.plot_date	Draw a graph that forces the axis to treat floating-point numbers as dates .
Axes.step	Draw a ladder diagram .
Axes.loglog	stay x Axis and y Plot on axis using logarithmic scaling .
Axes.semilogx	stay x Draw a graph with logarithmic scale on the axis .
Axes.semilogy	use y Plot logarithmic scale on axis .
Axes.fill_between	Fill the area between two horizontal curves .
Axes.fill_betweenx	Fill the area between two vertical curves .
Axes.bar	Draw a bar graph .
Axes.barh	Draw a horizontal bar chart .
Axes.bar_label	Mark bar chart .
Axes.stem	Create a stem chart .
Axes.eventplot	Draw the same parallel lines at a given location .
Axes.pie	Draw the pie chart .
Axes.stackplot	Plot stacking area .
Axes.broken_barh	Draw a horizontal sequence of rectangles .
Axes.vlines	At every x Draw from ymin To ymax The vertical line of .
Axes.hlines	In from xmin To xmax Each y Draw a horizontal line on .
Axes.fill	Draw filled polygons .
Axes.axhline	Add a horizontal line on the axis .
Axes.axhspan	Add a horizontal span on the shaft （ rectangular ）.
Axes.axvline	Add a vertical line on the axis .
Axes.axvspan	Add a vertical span on the axis （ rectangular ）.
Axes.axline	Add an infinite line .
Axes.acorr	draw x The autocorrelation of .
Axes.angle_spectrum	Draw the angle spectrum .
Axes.cohere	draw x and y Coherence between .
Axes.csd	Plot the cross spectral density .
Axes.magnitude_spectrum	Draw the amplitude spectrum .
Axes.phase_spectrum	Draw the phase spectrum .
Axes.psd	Plot the power spectral density .
Axes.specgram	Plot the spectrum .
Axes.xcorr	draw x and y Cross correlation between .
Axes.clabel	Mark contour map .
Axes.contour	Draw contours .
Axes.contourf	Draw a fill profile .
Axes.imshow	Display data as an image , That is to say 2D On the regular grid .
Axes.matshow	take 2D The values of a matrix or array are drawn as color coded images .
Axes.pcolor	Create a pseudo color map with an irregular rectangular grid .
Axes.pcolorfast	Create a pseudo color map with an irregular rectangular grid .
Axes.pcolormesh	Create a pseudo color map with an irregular rectangular grid .
Axes.spy	draw 2D Sparse pattern of the array .
Axes.get_xaxis_transform	Get for drawing x Axis labels , Conversion of tick marks and gridlines .
Axes.get_yaxis_transform	Get for drawing y Axis labels , Conversion of tick marks and gridlines .
Axes.get_data_ratio	Returns the aspect ratio of the scaled data .

Axes About the method of coordinate axis

Method	explain
Axes.axis	A convenient way to get or set some axis properties .
Axes.set_axis_off	close x and y Axis .
Axes.set_axis_on	Turn on x and y Axis .
Axes.set_frame_on	Set whether to draw axis rectangle patch .
Axes.get_frame_on	Gets whether the axis rectangle patch is drawn .
Axes.set_axisbelow	Sets whether the axis tick marks and grid lines are above or below the graph .
Axes.get_axisbelow	Gets whether the axis scale and grid line are above or below the graph .
Axes.grid	Add gridlines .
Axes.get_facecolor	Get the surface color of the shaft .
Axes.set_facecolor	Set the surface color of the shaft .
Axes.invert_xaxis	reverse x Axis .
Axes.xaxis_inverted	return x Whether the axis is along “ back ” Direction orientation .
Axes.invert_yaxis	reverse y Axis .
Axes.yaxis_inverted	return y Whether the axis is along “ back ” Direction orientation .
Axes.set_xlim	Set up x Axis range .
Axes.get_xlim	return x Axis range .
Axes.set_ylim	Set up y Axis range .
Axes.get_ylim	return y Axis range .
Axes.set_xbound	Set up x The upper and lower boundaries of the axis .
Axes.get_xbound	Returns... In ascending order x The upper and lower boundaries of the axis .
Axes.set_ybound	Set up y The upper and lower boundaries of the axis .
Axes.get_ybound	Returns... In ascending order y The upper and lower boundaries of the axis .
Axes.set_xlabel	Set up x The label of the shaft .
Axes.get_xlabel	obtain xlabel Text string .
Axes.set_ylabel	Set up y The label of the shaft .
Axes.get_ylabel	obtain ylabel Text string .
Axes.set_title(self, label, fontdict=None, loc=None, pad=None, *, y=None)	Set the title for the axis .fontdict：{‘fontsize’: rcParams[‘axes.titlesize’],‘fontweight’: rcParams[‘axes.titleweight’], ‘color’: rcParams[‘axes.titlecolor’],‘verticalalignment’: ‘baseline’,‘horizontalalignment’: loc}.loc : {‘center’, ‘left’, ‘right’}
Axes.get_title(self, loc=“center”)	Get axis title .left,center,right.
Axes.legend	Place a legend on the shaft .
Axes.get_legend	return Legend example , If no legend is defined , Then return to None.
Axes.get_legend_handles_labels(self, legend_handler_map=None)	Returns the handle and label of the legend
Axes.set_xscale	Set up x Axis scale .
Axes.get_xscale	return xaxis The scale of （ With str Express ）.
Axes.set_yscale	Set up y Axis scale .
Axes.get_yscale	return yaxis The scale of （ With str Express ）.
Axes.set_xticks	Set up xaxis Scale position of .
Axes.get_xticks	Return to data coordinates xaxis Scale position of .
Axes.set_xticklabels	Use the string label list to set xaxis The label of .
Axes.get_xticklabels	obtain xaxis Scale labels for .
Axes.get_xmajorticklabels	return xaxis Major scale labels for , As a list of Text.
Axes.get_xminorticklabels	return xaxis Sub scale labels for , As a list of Text.
Axes.get_xgridlines	return xaxis Grid lines as Line2Ds A list of .
Axes.get_xticklines	With x Is returned as a list xaxis The scale line of Line2D.
Axes.xaxis_date	Set axis scale and label , To move the x The data of the axis is regarded as the date .
Axes.set_yticks	Set up yaxis Scale position of .
Axes.get_yticks	Return to data coordinates yaxis Scale position of .
Axes.set_yticklabels	Use the string label list to set yaxis label .
Axes.get_yticklabels	obtain yaxis Scale labels for .
Axes.get_ymajorticklabels	return yaxis Major scale labels for , As a list of Text.
Axes.get_yminorticklabels	return yaxis Minor scale labels for , As a list of Text.
Axes.get_ygridlines	return yaxis Grid lines as Line2Ds A list of .
Axes.get_yticklines	return yaxis The scale mark of is used as Line2Ds A list of .
Axes.yaxis_date	Set axis scale and label , To move the y The data of the axis is regarded as the date .
Axes.minorticks_off	Remove the small clicking sound on the shaft .
Axes.minorticks_on	Show smaller scale on axis .
Axes.ticklabel_format	To configure ScalarFormatter Used for linear axes by default .
Axes.tick_params	Change tick marks , Appearance of tick mark labels and gridlines .
Axes.set_major_locator(self, locator)	Set the scale position .locator : ~matplotlib.ticker.Locator

Axes.set_xticks(self, ticks, minor=False)

Set up x Axis scale , By default, the scale also has a label .

• ticks: This parameter is x List of axis scale positions .
• minor: This parameter is used to set the primary or secondary tick marks

Axes.set_xticklabels(self, labels, fontdict=None, minor=False, **kwargs)

Set with string label list xaxis label .

• labels: This parameter is a list of string labels .
• fontdict: This parameter is the dictionary that controls the appearance of the scale label .
  The default value box is ：{‘fontsize’: rcParams[‘axes.titlesize’],
  ‘fontweight’: rcParams[‘axes.titleweight’],
  ‘verticalalignment’: ‘baseline’,
  ‘horizontalalignment’: loc}
• minor: The default value is ：False, Whether to set secondary tick mark labels instead of primary tick mark labels .

import numpy as np
import matplotlib.pyplot as plt


def main():
    fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    ax.plot(np.random.randn(1000).cumsum())
    ticks = ax.set_xticks([0, 250, 500, 750, 1000])
    label = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'], rotation=30, fontsize='small')
    ax.set_title('My')
    ax.set_xlabel("Stages")
    plt.show()


if __name__ == '__main__':
    main()

annotation annotate

annotate Used to add text annotations to data on graphics , And support the scribing tool with arrow , It is convenient for us to add description information in the right place .

Axes.annotate(s, xy, *args, **kwargs)

• s： The content of the comment text
• xy： Annotated coordinate points , Two dimensional tuples are shaped like (x,y)
• xytext： Coordinate point of annotation text , It's also a two-dimensional tuple , Default and xy identical
• xycoords： Coordinate system attribute of the annotated point , The allowed values are as follows

Property value	meaning
‘figure points’	Take the lower left corner of the drawing area as the reference , The unit is the number of points
‘figure pixels’	Take the lower left corner of the drawing area as the reference , The unit is the number of pixels
‘figure fraction’	Take the lower left corner of the drawing area as the reference , The unit is percentage
‘axes points’	Take the lower left corner of the sub drawing area as the reference , The unit is the number of points （ One figure There can be multiple axex, The default is 1 individual ）
‘axes pixels’	Take the lower left corner of the sub drawing area as the reference , The unit is the number of pixels
‘axes fraction’	Take the lower left corner of the sub drawing area as the reference , The unit is percentage
‘data’	With the annotated coordinate point xy For reference ( The default value is )
‘polar’	Do not use local data coordinate system , Use polar coordinate system

• textcoords ： Coordinate system properties of annotation text , Default and xycoords Same property value , It can also be set to different values . In addition to allowing input xycoords The attribute value , You can also enter the following two types ：

Property value	meaning
‘offset points’	Relative to the annotated point xy The offset （ The unit is dot ）
‘offset pixels’	Relative to the annotated point xy The offset （ Unit is pixel ）

• arrowprops： The style of the arrow ,dict（ Dictionaries ） Type data , If the attribute is not empty , An arrow will be drawn between the annotated text and the annotated point . If not set ’arrowstyle’ keyword , The following keywords are allowed ：

keyword	meaning
width	The width of the arrow （ The unit is dot ）
headwidth	The width of the head of the arrow （ spot ）
headlength	The length of the head of the arrow （ spot ）
shrink	The percentage of shrinkage at both ends of the arrow （ Account for the total length ）
?	Any keyword

If set ‘arrowstyle’ keyword , The above keywords cannot be used . The allowed values are ：

The style of the arrow	attribute
‘-’	None
‘->’	head_length=0.4,head_width=0.2
‘-[’	widthB=1.0,lengthB=0.2,angleB=None
’	-
'-	>’
‘<-’	head_length=0.4,head_width=0.2
‘<->’	head_length=0.4,head_width=0.2
'<	-’
'<	-
‘fancy’	head_length=0.4,head_width=0.4,tail_width=0.4
‘simple’	head_length=0.5,head_width=0.5,tail_width=0.2
‘wedge’	tail_width=0.3,shrink_factor=0.5

• annotation_clip : Boolean value , Optional parameters , The default is empty. . Set to True when , Only when the annotated point is in the sub drawing area can the annotation be drawn ; Set to False when , The annotation is drawn wherever the annotated point is . Only when the xycoords by ‘data’ when , A null default value is equivalent to True.

Example ： Set the color and indentation of the arrow

import numpy as np
import matplotlib.pyplot as plt
 
fig, ax = plt.subplots()
 
#  Draw a cosine curve 
t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = ax.plot(t, s, lw=2)
 
#  Draw a black , Arrows with indented ends 
ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
            xycoords='data',
            arrowprops=dict(facecolor='black', shrink=0.05)
            )
ax.set_ylim(-2, 2)
plt.show()

Example ： Draw notes with different coordinate systems

import numpy as np
import matplotlib.pyplot as plt
 
#  In steps 0.005 Draw a curve 
x = np.arange(0, 10, 0.005)
y = np.exp(-x/2.) * np.sin(2*np.pi*x)
 
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xlim(0, 10)
ax.set_ylim(-1, 1)
 
#  The coordinates of the data axis and the pixel of the annotated point 
xdata, ydata = 5, 0
xdisplay, ydisplay = ax.transData.transform_point((xdata, ydata))
 
#  Set the style of note text and arrow 
bbox = dict(boxstyle="round", fc="0.8")
arrowprops = dict(
    arrowstyle = "->",
    connectionstyle = "angle,angleA=0,angleB=90,rad=10")
 
#  Set offset 
offset = 72
# xycoords The default is 'data' Data axis coordinates , For coordinate points （5,0） Add notes 
#  The annotation text refers to the annotation point to set the offset , towards the left 2*72points, Up 72points
ax.annotate('data = (%.1f, %.1f)'%(xdata, ydata),
            (xdata, ydata), xytext=(-2*offset, offset), textcoords='offset points',
            bbox=bbox, arrowprops=arrowprops)
 
# xycoords Take the lower left corner of the drawing area as the reference , In pixels 
#  The annotation text refers to the annotation point to set the offset , towards the right 0.5*72points, Down 72points
disp = ax.annotate('display = (%.1f, %.1f)'%(xdisplay, ydisplay),
            (xdisplay, ydisplay), xytext=(0.5*offset, -offset),
            xycoords='figure pixels',
            textcoords='offset points',
            bbox=bbox, arrowprops=arrowprops)
 
 
plt.show()

Example ： Notes on polar coordinates , Drawing in polar coordinates , And set the annotated points in the polar coordinate system , Place annotation text as a percentage of the plot area .

import numpy as np
import matplotlib.pyplot as plt
 
#  Draw a polar coordinate , And then to 0.001 Step length , Draw a spiral curve 
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
r = np.arange(0,1,0.001)
theta = 2 * 2*np.pi * r
line, = ax.plot(theta, r, color='#ee8d18', lw=3)
 
#  The index is 800 Draw a dot at , And make notes 
ind = 800
thisr, thistheta = r[ind], theta[ind]
ax.plot([thistheta], [thisr], 'o')
ax.annotate('a polar annotation',
            xy=(thistheta, thisr),  #  The annotated points follow the polar coordinate system , The coordinates are angle and radius 
            xytext=(0.05, 0.05),    #  The annotation text is placed in the drawing area 0.05 Percent 
            textcoords='figure fraction',
            arrowprops=dict(facecolor='black', shrink=0.05),#  The arrow line is black , Indent both ends 5%
            horizontalalignment='left',#  Align the left and low ends of the note text to the specified position 
            verticalalignment='bottom',
            )
plt.show()

Mouse over to show hidden content annotate Realization

1. establish [ spot , notes ] Right list , By default , Comments are not visible
2. Every time a mouse movement is detected , Will register a function “on_move”
3. on_move The function iterates through each point and comment , If the mouse is now at one of the points , Make its associated comments visible , If not , Make it invisible .

import matplotlib.pyplot as plt
 
fig = plt.figure()
po_annotation = []
for i in range(0, 10):
    x = i
    y = x**2
    point, = plt.plot(x, y, 'o')
    annotation = plt.annotate(('x='+str(x), 'y='+str(y)), xy=(x+0.1, y+0.1), xycoords='data', xytext=(x+0.7, y+0.7),
                                textcoords='data', horizontalalignment="left",
                                arrowprops=dict(arrowstyle="simple",connectionstyle="arc3,rad=-0.1"),
                                bbox=dict(boxstyle="round", facecolor="w",edgecolor="0.5", alpha=0.9)
                                )
    annotation.set_visible(False)
    po_annotation.append([point, annotation])
 
def on_move(event):
    visibility_changed = False
    for point, annotation in po_annotation:
        should_be_visible = (point.contains(event)[0] == True)
        # print(point.contains(event)[0])
 
        if should_be_visible != annotation.get_visible():
            visibility_changed = True
            annotation.set_visible(should_be_visible)
 
    if visibility_changed:
        plt.draw()
 
on_move_id = fig.canvas.mpl_connect('motion_notify_event', on_move)
 
plt.show()