void Plot_Wave(uint16_t *Data,uint16_t MAX,uint16_t x,uint16_t y,uint16_t Start) { // uint16_t Data_Max;//存放数组中的最大值 // uint16_t Ymax,Ymin; uint16_t TemData[200]; uint16_t i; float Ratio;//用来对数据进行归一化显示 if(MAX>200) { Ratio = (float)(200/MAX); } else {Ratio = 1;} for(i=0:i<200;i++) { if((uint16_t)(Ratio*Data[Start+i])>100) { TemData[i] = (uint16_t)(Ratio*Data[Start+i]); } else { TemData[i] = -(uint16_t)(Ratio*Data[Start+i]); } } LCD_SetBackColor(WHITE); ILI9341_Clear(0,0,360,240); Plot_Graph(MAX,x,y); LCD_SetTextColor(RED); for(i=0;i<199;i++) { ILI9341_DrawLine(x+100+i,y+TemData[i],x+101+i,y+TemData[i+1]); } }

这是一个绘制波形的函数，使用了ILI9341液晶屏和一个存储波形数据的数组。函数的输入参数包括Data数组、数组的最大值MAX、绘制波形的起始坐标(x, y)以及Start参数用于指定从Data数组的哪个位置开始绘制波形。

首先，函数会根据MAX值计算一个归一化比例Ratio。如果MAX大于200，Ratio将等于200/MAX，否则Ratio为1。

然后，函数会遍历长度为200的临时数组TemData，在每个位置上计算归一化后的数据，并存储到TemData数组中。如果归一化后的数据大于100，将存储正值，否则存储负值。

接下来，函数会使用LCD_SetBackColor函数设置背景颜色为白色，并清除整个屏幕。

然后，函数会调用Plot_Graph函数绘制一个坐标轴。

最后，函数会使用ILI9341_DrawLine函数在屏幕上绘制波形，通过连接相邻的点来实现。

请注意，该代码中有一些被注释掉的代码段，可能是之前的尝试或者备用代码。

相关问题

#include "arduinoFFT.h" arduinoFFT FFT = arduinoFFT(); const uint16_t samples = 64; const double signalFrequency = 1000; const double samplingFrequency = 5000; const uint8_t amplitude = 100; double vReal[samples]; double vImag[samples]; #define SCL_INDEX 0x00 #define SCL_TIME 0x01 #define SCL_FREQUENCY 0x02 #define SCL_PLOT 0x03 void setup() { Serial.begin(115200); while(!Serial); Serial.println("Ready"); } void loop() { double cycles = (((samples-1) * signalFrequency) / samplingFrequency); for (uint16_t i = 0; i < samples; i++) { vReal[i] = int8_t((amplitude * (sin((i * (twoPi * cycles)) / samples))) / 2.0); vImag[i] = 0.0; } Serial.println("Data:"); PrintVector(vReal, samples, SCL_TIME); FFT.Windowing(vReal, samples, FFT_WIN_TYP_HAMMING, FFT_FORWARD); /* Weigh data */ Serial.println("Weighed data:"); PrintVector(vReal, samples, SCL_TIME); FFT.Compute(vReal, vImag, samples, FFT_FORWARD); /* Compute FFT */ Serial.println("Computed Real values:"); PrintVector(vReal, samples, SCL_INDEX); Serial.println("Computed Imaginary values:"); PrintVector(vImag, samples, SCL_INDEX); FFT.ComplexToMagnitude(vReal, vImag, samples); /* Compute magnitudes */ Serial.println("Computed magnitudes:"); PrintVector(vReal, (samples >> 1), SCL_FREQUENCY); double x; double v; FFT.MajorPeak(vReal, samples, samplingFrequency, &x, &v); Serial.print(x, 6); Serial.print(", "); Serial.println(v, 6); while(1); } void PrintVector(double *vData, uint16_t bufferSize, uint8_t scaleType) { for (uint16_t i = 0; i < bufferSize; i++) { double abscissa; switch (scaleType) { case SCL_INDEX: abscissa = (i * 1.0); break; case SCL_TIME: abscissa = ((i * 1.0) / samplingFrequency); break; case SCL_FREQUENCY: abscissa = ((i * 1.0 * samplingFrequency) / samples); break; } Serial.print(abscissa, 6); if(scaleType==SCL_FREQUENCY) Serial.print("Hz"); Serial.print(" "); Serial.println(vData[i], 4); } Serial.println(); } 根据此历程中fft转换的方法修改oled代码

根据你提供的代码，你想要修改 OLED 显示代码来显示 FFT 转换的结果。我已经修改了代码，添加了 OLED 显示的相关部分。请注意，这里假设你已经连接了 OLED 显示屏并正确配置了 Arduino 板。

#include <Wire.h>
#include <Adafruit_GFX.h>
#include <Adafruit_SSD1306.h>
#include "arduinoFFT.h"

#define OLED_RESET 4
Adafruit_SSD1306 display(OLED_RESET);

arduinoFFT FFT = arduinoFFT();
const uint16_t samples = 64;
const double signalFrequency = 1000;
const double samplingFrequency = 5000;
const uint8_t amplitude = 100;

double vReal[samples];
double vImag[samples];

void setup()
{
  Serial.begin(115200);
  while (!Serial)
    ;
  Serial.println("Ready");

  display.begin(SSD1306_SWITCHCAPVCC, 0x3C);
  display.clearDisplay();
  display.setTextColor(WHITE);
  display.setTextSize(1);
}

void loop()
{
  double cycles = (((samples - 1) * signalFrequency) / samplingFrequency);
  for (uint16_t i = 0; i < samples; i++)
  {
    vReal[i] = int8_t((amplitude * (sin((i * (twoPi * cycles)) / samples))) / 2.0);
    vImag[i] = 0.0;
  }

  display.clearDisplay();

  Serial.println("Data:");
  PrintVector(vReal, samples, SCL_TIME);

  FFT.Windowing(vReal, samples, FFT_WIN_TYP_HAMMING, FFT_FORWARD);
  Serial.println("Weighed data:");
  PrintVector(vReal, samples, SCL_TIME);

  FFT.Compute(vReal, vImag, samples, FFT_FORWARD);
  Serial.println("Computed Real values:");
  PrintVector(vReal, samples, SCL_INDEX);
  Serial.println("Computed Imaginary values:");
  PrintVector(vImag, samples, SCL_INDEX);

  FFT.ComplexToMagnitude(vReal, vImag, samples);
  Serial.println("Computed magnitudes:");
  PrintVector(vReal, (samples >> 1), SCL_FREQUENCY);

  double x;
  double v;
  FFT.MajorPeak(vReal, samples, samplingFrequency, &x, &v);
  Serial.print(x, 6);
  Serial.print(", ");
  Serial.println(v, 6);

  // 显示 FFT 转换的结果
  display.setCursor(0, 0);
  display.print("Frequency: ");
  display.println(x, 6);
  display.print("Magnitude: ");
  display.println(v, 6);

  display.display();

  while (1)
    ;
}

void PrintVector(double *vData, uint16_t bufferSize, uint8_t scaleType)
{
  for (uint16_t i = 0; i < bufferSize; i++)
  {
    double abscissa;
    switch (scaleType)
    {
    case SCL_INDEX:
      abscissa = (i * 1.0);
      break;
    case SCL_TIME:
      abscissa = ((i * 1.0) / samplingFrequency);
      break;
    case SCL_FREQUENCY:
      abscissa = ((i * 1.0 * samplingFrequency) / samples);
      break;
    }
    Serial.print(abscissa, 6);
    if (scaleType == SCL_FREQUENCY)
      Serial.print("Hz");
    Serial.print(" ");
    Serial.println(vData[i], 4);
  }
  Serial.println();
}

在这个修改后的代码中，我添加了 OLED 显示的相关部分。在 `loop` 函数中，我们在每次进行 FFT 转换后，通过 `display` 对象显示频率和幅值的结果。

请确保你已经连接了 OLED 显示屏，并将其引脚与 Arduino 板正确连接。如果需要，你可以根据 OLED 显示屏的具体型号和引脚配置进行相应的修改。

希望这个修改后的代码能够满足你的需求！如果你还有其他问题，请随时提问。

翻译这段程序并自行赋值调用：import matplotlib.pyplot as plt import numpy as np import sklearn import sklearn.datasets import sklearn.linear_model def plot_decision_boundary(model, X, y): # Set min and max values and give it some padding x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1 y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1 h = 0.01 # Generate a grid of points with distance h between them xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Predict the function value for the whole grid Z = model(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # Plot the contour and training examples plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) plt.ylabel('x2') plt.xlabel('x1') plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral) def sigmoid(x): s = 1/(1+np.exp(-x)) return s def load_planar_dataset(): np.random.seed(1) m = 400 # number of examples N = int(m/2) # number of points per class print(np.random.randn(N)) D = 2 # dimensionality X = np.zeros((m,D)) # data matrix where each row is a single example Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue) a = 4 # maximum ray of the flower for j in range(2): ix = range(Nj,N(j+1)) t = np.linspace(j3.12,(j+1)3.12,N) + np.random.randn(N)0.2 # theta r = anp.sin(4t) + np.random.randn(N)0.2 # radius X[ix] = np.c_[rnp.sin(t), rnp.cos(t)] Y[ix] = j X = X.T Y = Y.T return X, Y def load_extra_datasets(): N = 200 noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3) noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2) blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6) gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None) no_structure = np.random.rand(N, 2), np.random.rand(N, 2) return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure

这段程序是一个分类模型的辅助函数，包括了绘制决策边界、sigmoid函数和加载数据集的函数。具体实现如下：

import matplotlib.pyplot as plt
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.linear_model

def plot_decision_boundary(model, X, y):
    # 设置最小值和最大值，并给它们一些填充
    x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
    y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
    h = 0.01

    # 生成一个网格，网格中点的距离为h
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

    # 对整个网格预测函数值
    Z = model(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    # 绘制轮廓和训练样本
    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
    plt.ylabel('x2')
    plt.xlabel('x1')
    plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)

def sigmoid(x):
    s = 1 / (1 + np.exp(-x))
    return s

def load_planar_dataset():
    np.random.seed(1)
    m = 400  # 样本数量
    N = int(m / 2)  # 每个类的样本数量

    # 生成数据集
    D = 2  # 特征维度
    X = np.zeros((m, D))  # 特征矩阵
    Y = np.zeros((m, 1), dtype='uint8')  # 标签向量

    a = 4  # 花的最大半径

    for j in range(2):
        ix = range(N*j, N*(j+1))
        t = np.linspace(j*3.12, (j+1)*3.12, N) + np.random.randn(N)*0.2  # theta
        r = a*np.sin(4*t) + np.random.randn(N)*0.2  # radius
        X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
        Y[ix] = j

    X = X.T
    Y = Y.T

    return X, Y

def load_extra_datasets():
    N = 200
    noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)
    noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)
    blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)
    gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None)
    no_structure = np.random.rand(N, 2), np.random.rand(N, 2)

    return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure

这段程序中包含了以下函数：

- `plot_decision_boundary(model, X, y)`：绘制分类模型的决策边界，其中`model`是分类模型，`X`是特征矩阵，`y`是标签向量。
- `sigmoid(x)`：实现sigmoid函数。
- `load_planar_dataset()`：加载一个二维的花瓣数据集。
- `load_extra_datasets()`：加载五个其他数据集。