Python matplotlib drawing parameter summary

0 Overview

Firstly, establish the canvas and recommend using fig, ax= Plt. subplots , assign all drawing tasks to the ax object. Note that unless a 1 * 1 canvas partition is used, ax will be a two-dimensional array and different positions of ax objects need to be obtained in the form of an array.

1. Important factors for adjusting external auxiliary lines and legends in images

1. Main title fig.suptitle("title")
2. Subtitle ax.set_title("title)
3. Axis labels
   ax.set_xlabel([array]) ax.set_ylabel([array])
4. Coordinate axis display range
   ax.set_xlim([lower, upper]) ax.set_ylim([lower, upper])
5. Axis position
   ax.spines["left|top|right|bottom"].set_position(("data|outward,axes",value))
6. Coordinate axis display ax.spines["left|top|right|bottom"].set_visible(True|False)
7. Axis color
   ax.spines["left|top|right|bottom"].set_color(“color”)
8. Legend ax.legend(loc="upper right|upper left}bottom left| bottom right| left|right|top|bottom|best")
   This property requires defining label variables in each image curve
9. Grid Ax. grid (alpha= 0.5) alpha sets transparency
10. Log coordinate display
    ax.set_xscale('log')
ax.set_yscale('log')

Example, drawing sigmoid function.

from matplotlib import pyplot as plt
%matplotlib inline
sigma_x = np.arange(-10,10,0.1)
sigma_y = 1 /(1 + np.power(np.e, -sigma_x))
fig, ax = plt.subplots(1,1,figsize=(12,4),facecolor='whitesmoke', edgecolor='gray')
ax.plot(sigma_x, sigma_y, lw=1, ls= '-', color='blue',label="sigmoid core function")
fig.suptitle("title")
ax.legend(loc="upper left")
ax.set_xlabel("t")
ax.set_ylabel("sigmoid")
ax.set_xticks(np.arange(-10,10.1,5))
ax.set_yticks(np.arange(0,1.2,0.2))
ax.set_xlim(-10,10)
ax.set_ylim(-0.1,1.1)
ax.set_title('sigmoid function show')
ax.axhline(0, color='grey', linewidth=1)
ax.axvline(0, color='grey', linewidth=1)
ax.hlines(0.5,-12,12, ls=':', color='gray')
ax.hlines(1.0,-12,12, ls=':', color='gray')
plt.show()

• The complete code can be found here (GitHub repository)

2. Important factors for adjusting the internal lines of an image

1. Draw horizontal and vertical auxiliary lines
   ax.hlines(y,x_left,x_right, ls=':', color='gray')
ax.vlines(x,y_bottom,y_up, ls=':', color='gray')
2. Draw the horizontal and vertical axes
   ax.axhline(pos, color='grey', linewidth=1)
ax.axvline(pos, color='grey', linewidth=1)
3. Draw different curves
   ax.polt()
ax.scatter()
4. Fill the middle position of the curve
   ax.fill_between(x,y1,y2)
5. Add curve annotations
   plt.annotate(text ,xy,xytext,arrowprops = {'headwidth':10,'facecolor':'r'},fontsize = 15)
   Xy represents arrow position, xytext represents text position
   ax.text(s,x,y,fontsize,color)
   The above two functions are often used together with the for loop

Annotate Example.

x = np.linspace(0, 10, 30)

plt.plot(x, np.sin(x),c = '#0ffef9',label = 'sin(x)')
plt.annotate(text = 'bottom point',xy = (4.65,-1),xytext = (4.2,0),
       arrowprops = {'headwidth':10,'facecolor':'r'},fontsize = 15)
plt.show()

Example, knn grouping.

from matplotlib import pyplot as plt
import matplotlib
plt.rcParams['font.sans-serif']=['SimHei'] #specify default font SimHei for blackbody 
plt.rcParams['axes.unicode_minus']=False   #used to display negative signs normally 
colors = ['red', 'purple', 'blue', 'green', 'lime']
shapes = ['+', 'o', '>', '*', '.']
# create 5 different list containing 5 classes of points
upper_list = []
for i in range(k):
new_list = []
upper_list.append(new_list)
# assign each point into its corresponding class
data_with_lab = zip(data, label)
for dat, lab in data_with_lab:
for i in range(k): 
  if lab == i:
      upper_list[i].append(dat.tolist())
      
fig, ax = plt.subplots(1,1,facecolor='whitesmoke',edgecolor='grey',
                 dpi=120,figsize=(8,6))
#fig.suptitle("knn results visualization",fontsize=18, color='red')
ax.set_title('knn with groups')
for i in range(k):
ax.scatter(np.array(upper_list[i])[:,0],
         np.array(upper_list[i])[:,1],
         color=colors[i], 
         marker=shapes[i],
         label="class-"+str(i))
# plot the unassigned point
ax.plot(target[0],target[1],color='black',marker='<',label="untold")
ax.text(target[0]-1.8,target[1]+1.2,
  'class-'+str(target_lab[0]),
  fontsize=8)
ax.legend()
ax.grid(alpha=0.2)

• The complete code can be found here (GitHub repository)

3 Special image drawing

1. Draw a heat map through a matrix
   plt.imshow(matrix)
plt.matshow(matrix)

Example: Drawing a fusion matrix heat map.

from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_predict
y_train_pred = cross_val_predict(forest_clf, X_train_scaled, y_train, cv=5)
conf_mat = confusion_matrix(y_train ,y_train_pred)
norm_conf_mat = conf_mat / conf_mat.sum(axis=1, keepdims=True)
np.fill_diagonal(norm_conf_mat, 0)
plt.matshow(norm_conf_mat, cmap=plt.get_cmap('autumn_r'))
plt.xticks(range(len(norm_conf_mat)),np.arange(0,10,1),rotation=30)
plt.yticks(range(len(norm_conf_mat[0])),np.arange(0,10,1),rotation=30)
plt.xlabel("predict")
plt.ylabel('real')
for x_pos, val in enumerate(norm_conf_mat):
for y_pos , vals in enumerate(val):
  plt.text(x_pos, y_pos, np.round(vals,5),
           va='center',ha='center',fontsize=5)

• The complete data processing code can be found here (GitHub repository)

4. Image classification and coloring

1. Grid x and y
   xx,yy = np.meshgrid(x,y)
2. Classify coloring based on function values
   ax,contourf(xx,yy,zz,cmap)

Example, Moon data classification.

from sklearn.tree import DecisionTreeClassifier
from matplotlib import pyplot as plt
# train one default CART with moon datasets
tree_clf2 = DecisionTreeClassifier(max_depth=10)
tree_clf2.fit(X,y)
x_axis = np.arange(-2,3,0.1)
y_axis = np.arange(-1,1.5,0.1)
xx, yy = np.meshgrid(x_axis,y_axis)
zz = np.zeros_like(xx)
for ly in range(len(x_axis)):
for lx in range(len(y_axis)):
  zz[lx, ly] = tree_clf2.predict([[x_axis[ly],y_axis[lx]]])
fig,ax = plt.subplots(1,1,facecolor='whitesmoke',edgecolor='grey',
                figsize=(4,3))
ax.contourf(xx,yy,zz,cmap=plt.cm.Spectral)
class_num = 2
marker_name = [4,"*"]
label_name = ['class0','class1']
color_name = ['darkblue','orange']
for index in range(class_num):
ax.scatter(X[np.where(y==index),0],X[np.where(y==index),1],
         label=label_name[index],color=color_name[index],
         marker=marker_name[index],lw=0.2)
ax.legend()
ax.grid(alpha=0.5)