如何将 plot （22、3、2）矩阵列表列表作为散点 plot？

Question

I am trying to plot the below sensitivity matrix into a plot that consists of each list as sub-plot in it. Preferably as a function and look like this one in seaborn tutorial.

I tried a similar way to plot the list of list but it only give me a single plot without subplots:

def scatter_plot(sen1_obs):
    
    x = []

y = []
for i in sen1_obs:
    x.append(i[0])
    y.append(i[1])
plt.scatter(x,y)
plt.show()

And execute:

scatter_plot(sen1_obs)

My list look like this:

sen1_obs
Out[2]: 
[array([[ 0.00040725,  0.00011072],
        [ 0.0008145 ,  0.00022144],
        [-0.00040725, -0.00011072]]),
 array([[ 8.32091781e-04, -8.06469105e-05],
        [ 1.66418356e-03, -1.61293821e-04],
        [-8.32091781e-04,  8.06469105e-05]]),
 array([[ 0.00121915,  0.00033146],
        [ 0.00243829,  0.00066291],
        [-0.00121915, -0.00033146]]),
 array([[ 0.00252797, -0.00024503],
        [ 0.00505593, -0.00049006],
        [-0.00252797,  0.00024503]]),
 array([[ 0.00202743,  0.00055121],
        [ 0.00405486,  0.00110242],
        [-0.00202743, -0.00055121]]),
 array([[ 0.00430218, -0.00041706],
        [ 0.00860436, -0.00083411],
        [-0.00430218,  0.00041706]]),
 array([[ 3.08460052e-04,  8.38668514e-05],
        [ 6.16920103e-04,  1.67733703e-04],
        [-3.08460052e-04, -8.38668514e-05]]),
 array([[ 0.00132295, -0.00012862],
        [ 0.0026459 , -0.00025724],
        [-0.00132295,  0.00012862]]),
 array([[ 0.00089098,  0.00024225],
        [ 0.00178196,  0.0004845 ],
        [-0.00089098, -0.00024225]]),
 array([[ 0.00402754, -0.00039161],
        [ 0.00805507, -0.00078323],
        [-0.00402754,  0.00039161]]),
 array([[ 0.00149702,  0.00040703],
        [ 0.00299405,  0.00081405],
        [-0.00149702, -0.00040703]]),
 array([[ 0.00665672, -0.00064722],
        [ 0.01331343, -0.00129443],
        [-0.00665672,  0.00064722]]),
 array([[ 0.00879632,  0.00016573],
        [ 0.01759264,  0.00033147],
        [-0.00879632, -0.00016573]]),
 array([[ 0.00728856, -0.00055898],
        [ 0.01457712, -0.00111797],
        [-0.00728856,  0.00055898]]),
 array([[ 0.00913137, -0.00029702],
        [ 0.01826274, -0.00059403],
        [-0.00913137,  0.00029702]]),
 array([[ 0.00463152,  0.00026766],
        [ 0.00926303,  0.00053532],
        [-0.00463152, -0.00026766]]),
 array([[ 0.00315   ,  0.00014372],
        [ 0.0063    ,  0.00028743],
        [-0.00315   , -0.00014372]]),
 array([[ 0.00607034,  0.00023261],
        [ 0.01214067, 

Answer 1

You can try something like this. I don't have a great way to use your data so disregard my first few lines of code that are making the data I am working with. But, essentially what you are doing is making a 5x5 grid to house your 22 subplots. You then create a list of lists for their positions in the subplot (we'll call it pos ). You then iterate over all the arrays in your variable en1_obs and all the lists within your arrays. Delete the left over subplots (25-22 so 3 unused subplots) and you have your figure:

######### This is just code to make a (22,3,2) matrix to work with #########
import random
en1_obs = []
for x in range(22):
    longlists = []
    for y in range(3):
        lists = []
        for z in range(2):
            lists.append(random.randint(1, 60))
        longlists.append(lists)
    en1_obs.append(np.array(longlists))
############################################################################

### This is the important code

plt.rcParams["figure.figsize"] = (20,10)    
width = 5
height = 5
fig, ax = plt.subplots(height,width)
pos = [[x,y] for x in range(height) for y in range(width)]

count = 0
for i in en1_obs:
    ax[pos[count][0],pos[count][1]].scatter(i[:, 0], i[:, 1])
    count +=1
fig.delaxes(ax[4,2])
fig.delaxes(ax[4,3])
fig.delaxes(ax[4,4])
plt.show()

Answer 2

The first point is to transform the data, so I am transforming it by changing the dimensions. The graph is drawn in a loop procedure with that transformed array. Empty graphs are manually deleted, so please adjust them. The number of data presented does not match the number of graphs, but please adjust the number of graphs to match my code to your data.

fig, axs = plt.subplots(5, 4, figsize=(9,9))
fig.subplots_adjust(wspace=0.5,hspace=0.5)

def scatter_plot(data):
    for d,ax in zip(data, axs.ravel()):
        xy = d.flatten().reshape(2,3, order='F')
        ax.scatter(xy[0], xy[1])
    fig.delaxes(axs[4,1])
    fig.delaxes(axs[4,2])
    fig.delaxes(axs[4,3])
    plt.show()

scatter_plot(data)