@飞 这个和spi flash读取数据是一样的，首先配置为读操作后，给起始地址位，9249就会按地址连续地返回8位数据。

AD9249有SPI模式，用该连接方式会简单很多

这个是对应芯片手册上的时序要求

基本只要按照芯片手册上的寄存器模式进行参数配置

https://www.analog.com/media/en/technical-documentation/data-sheets/AD9249.pdf

这个是AD9249的芯片手册，重点关注：9页+26页（时序余量），29~32页（参数配置说明）

@刘崇硕
因为当时没法发视频，就只能通过检测内核的方法展示。

动图是50MHz频率下在anlog开发板下进行M0分部调试(开发板蓝色的灯)的演示

以及全速运行的演示

资源增加（假设原有资源消耗70%以上）理论上可以使它工作频率上升，或者添加时序约束也能提升它的工作频率，另外可能还有配套综合工具的影响。

手边材料和设备有限，只能测试了一下典型的72MHz工作频率。经测试，在安路上能正常工作。如图所示：

建议检查一下DesignStart版本号

以及附上安路的移植示例:https://gitee.com/verimake/SparkRoad-V/tree/master/demo/13_EG4S20withM0

# -*- coding: UTF-8 -*-
import numpy as np

init_num = 2041
bit_num = 4
random_num = 1
while random_num:
    random_num = init_num*init_num
    shift_num = int(random_num / np.power(10, int(bit_num/2)))
    bit1 = shift_num / np.power(10, bit_num)
    bit2 = int(bit1)
    random_num = int((bit1-bit2) * np.power(10, bit_num))
    init_num = random_num
    print(random_num)

另外一个想法是，将所需取中位数字的数进行小数点移位并强制整形，最后通过减法保留所需要的数值

# -*- coding: UTF-8 -*-
def isolated(squ_num):
    squ_list = map(eval, str(squ_num))
    # print(squ_list)
    list_len = len(squ_list)
    # print(list_len)
    if (list_len % 2) != 0:
        squ_list.append(0)

    # print(squ_list)
    mid_list = []
    list_len = len(squ_list)
    mid_num = len(squ_list) / 2
    for x in range(mid_num - 2, mid_num + 2):
        mid_list.append(squ_list[x])

    # print(mid_list)
    cyc_num = int(''.join(map(str, mid_list)))
    print(cyc_num)
    return cyc_num

initial_num = 2041
times = 1
cyc_num = isolated(initial_num * initial_num)

while True:
    if cyc_num == 0:
        break
    else:
        cyc_num = isolated(cyc_num * cyc_num)
        times = times + 1

print(times, 'random numbers')

我的想法是避免取余操作，以减少程序的计算复杂度，所以首先通过list.map实现平方后的值作为字符逐位存入一个list中；紧接着通过len()对平方后的位数进行奇偶判断，以决定是否需要补零；最后同样通过map的方法实现取出中位数据的拼接并强制整形。
通过循环实现连续操作计算，最后得到所有的随机数以及个数。

from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)
data = pd.DataFrame(pd.read_excel('chinese.xlsx'))
# ax.scatter(data['全白'], data['波纹'], data['直线'], c='r')
time = np.array(data[['全白', '波纹', '直线']])
clf = KMeans(n_clusters=3)
clf = clf.fit(time)
clf.cluster_centers_
data['label'] = clf.labels_
data0 = data.loc[data['label'] == 0]
data1 = data.loc[data['label'] == 1]
data2 = data.loc[data['label'] == 2]
ax.scatter(data0['全白'], data0['波纹'], data0['直线'], c='r')
ax.scatter(data1['全白'], data1['波纹'], data1['直线'], c='b')
ax.scatter(data2['全白'], data2['波纹'], data2['直线'], c='g')
ax.legend()
ax.set_xlim(0, 100)
ax.set_ylim(0, 100)
ax.set_zlim(0, 100)
ax.set_xlabel('White')
ax.set_ylabel('Wave')
ax.set_zlabel('Line')
plt.legend()
plt.show()

time = np.array(data0[['全白', '波纹', '直线']])
clf = KMeans(n_clusters=3)
clf = clf.fit(time)
clf.cluster_centers_
data0['label'] = clf.labels_
print(data0)

0.9534194398757392

0.9755173917668096

0.9767573373910827

只是做了功能实现，没有注意编码风格，各位大佬轻拍

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import train_test_split

def trance(raw_list):
    a = []
    for i in range(0, len(raw_list), 1):
        b = raw_list[i:i+1]
        a.append(b)
    return a

data = pd.read_excel('rawdata.xlsx')
X = data['直径']
Y = data['木料']
X = trance(X.tolist())
Y = trance(Y.tolist())
x = np.array(X)

model = LinearRegression()
model1 = model.fit(X, Y)
b1 = model1.intercept_
w1 = model1.coef_
y1 = w1*X+b1
plt.subplot(3, 1, 1)
plt.scatter(X, Y, marker='o')
plt.plot(X, y1, color='green')
print(model.score(X, Y))

x1 = np.power(x, 2)
X1 = x1.tolist()
model2 = model.fit(X1, Y)
b2 = model2.intercept_
w2 = model2.coef_
y2 = w2*X1+b2
plt.subplot(3, 1, 2)
plt.scatter(X1, Y, marker='o')
plt.plot(X1, y2, color='red')
print(model.score(X1, Y))
x1 = np.power(x, 3)
X1 = x1.tolist()
model2 = model.fit(X1, Y)
b2 = model2.intercept_
w2 = model2.coef_
y3 = w2*X1+b2
plt.subplot(3, 1, 3)
plt.scatter(X1, Y, marker='o')
plt.plot(X1, y3, color='black')
print(model.score(X1, Y))
plt.xlabel(' Diameter ')
plt.ylabel(' Material area ')
plt.legend()
plt.show()