MATLAB Dynamic Curve Plotting: Real-Time Updates, Clear Visibility of Data Changes

# Introduction to MATLAB Dynamic Curve Plotting: Real-Time Updates and Clear Data Visualization

MATLAB dynamic curve plotting is a powerful technique that allows users to visualize data in real-time. It is widely used across various fields, including data analysis, scientific research, and engineering design. With dynamic curve plotting, users can monitor data streams, identify trends, and make timely decisions.

Dynamic curve plotting involves real-time data acquisition, processing, and visualization. MATLAB offers a range of functions and tools that simplify these tasks, making them efficient. Using these tools, users can create interactive charts that allow them to zoom, pan, and adjust curves to get the best view of the data.

# Theoretical Basis of MATLAB Dynamic Curve Plotting

### Real-Time Data Acquisition and Processing

#### Data Acquisition

Real-time data acquisition is the foundation of dynamic curve plotting. MATLAB provides a variety of data acquisition tools, such as the `daqread` function, which is used to read data from data acquisition cards or sensors. The data acquisition process typically involves the following steps:

- **Configuring data acquisition devices:** Setting parameters such as sampling rate, channels, and trigger conditions.
- **Starting data acquisition:** Using the `daqread` function to begin data acquisition.
- **Reading data:** Reading data from the acquisition device and storing it in MATLAB variables.

% Configuring data acquisition devices
daq = daq.createSession('ni');
daq.addAnalogInputChannel('Dev1', 0, 'Voltage');
daq.Rate = 1000; % Sampling rate at 1000 Hz

% Starting data acquisition
daq.startBackground();

% Reading data
data = daq.readData();

% Stopping data acquisition
daq.stop();

#### Data Preprocessing

The raw data acquired often ***mon preprocessing steps include:

- **Filtering:** Using digital filters to remove noise.
- **Detrending:** Removing trends or baseline drift from the data.
- **Normalization:** Scaling or normalizing the data to a specific range.

% Filtering
data = filter(b, a, data); % Using a Butterworth filter for noise reduction

% Detrending
data = detrend(data); % Removing linear trends

% Normalization
data = (data - min(data)) / (max(data) - min(data)); % Normalizing to the [0, 1] range

### Principles and Algorithms of Curve Plotting

#### Principles of Curve Plotting

Dynamic curve plotting involves the real-time updating and plotting of data. MATLAB uses double buffering techniques to achieve smooth curve plotting:

- **Front buffer:** Stores newly acquired data and processes it.
- **Back buffer:** Stores data to be plotted and displays it in the graphic window.

When new data is available, MATLAB adds it to the front buffer and triggers an event to update the back buffer. After the update is complete, the content of the back buffer is swapped to the front buffer and displayed in the graphic window.

#### Algorithms of Curve Plotting

MATLAB provides various curve plotting algorithms, including:

- **Linear interpolation:** Connecting adjacent data points with straight lines.
- **Spline interpolation:** Connecting data points with smooth curves.
- **Bezier curves:** Connecting data points with quadratic or cubic Bezier curves.

The choice of algorithm depends on the desired smoothness and accuracy of the curve.

% Using linear interpolation to plot a curve
plot(x, y, 'r-', 'LineWidth', 2); % Red solid line, line width of 2

% Using spline interpolation to plot a curve
plot(x, y, 'b-', 'LineWidth', 2); % Blue solid line, line width of 2

% Using Bezier curves to plot a curve
plot(x, y, 'g-', 'LineWidth', 2); % Green solid line, line width of 2

# Real-Time Data Acquisition and Preprocessing

#### Real-Time Data Acquisition

Real-time data acquisition is the basis of dynamic curve plotting and requires using appropriate sensors or data acquisition devices to obtain real-time data. MATLAB offers a variety of functions for data acquisition, such as `daqread` and `serial`. These functions allow users to configure data acquisition parameters, such as sampling rate, number of channels, and data type.

% Using daqread function to acquire data from a data acquisition card
data = daqread('myDAQ', 1000, 'Voltage');

% Using serial function to acquire data from a serial port
data = serial('COM1', 'BaudRate', 9600, 'DataBits', 8, 'Parity', 'none', 'StopBits', 1);
fopen(data);
data = fread(data, 1000);
fclose(data);

#### Data Preprocessing

Before plotting a curve, acquired data often needs to be preprocessed to remove noise, anomalies, and unnecessary trends. MATLAB provides various data preprocessing functions, such as `filter`, `detrend`, and `interp1`.

% Using filter function to remove noise
filteredData = filter('lowpass', data, 0.1);

% Using detrend function to remove linear trends
detrendedData = detrend(data);

% Using interp1 function to interpolate missing data
interpolatedData = interp1(1:length(data), data, linspace(1, length(data), 1000));

### Curve Plotting and Updating

#### Curve Plotting

After preprocessing the data, you can use the `plot` or `scatter` function to plot curves. The `plot` function draws a line chart connecting points, while the `scatter` function draws discrete points.

% Using plot function to draw a line chart
plot(time, data);

% Using scatter function to draw a scatter plot
scatter(time, data);

#### Curve Updating

The key to dynamic curve plotting is real-time updating of the curve. MATLAB provides the `animatedline` function, which allows users to create animated curves and automatically update the curve when data is updated.

% Creating an animated curve object
animatedLine = animatedline;

% Real-time updating of the curve
while true
     % Obtaining new data
     newData = daqread('myDAQ', 1);
     % Updating curve data
     addpoints(animatedLine, time, newData);
     % Drawing the curve
     drawnow;
end

### Interactive Operations and Visualization

#### Interactive Operations

MATLAB provides a variety of interactive operation tools that allow users to zoom, pan, and rotate curves. These tools can be used through the graphical user interface (GUI) or programmatically.

% Using the zoom function to zoom in on a curve
zoom on;

% Using the pan function to pan a curve
pan on;

% Using the rotate3d function to rotate a curve
rotate3d on;

#### Visualization

In addition to basic curve plotting, MATLAB also offers various visualization tools, such as `colorbar`, `legend`, and `title`. These tools can help users enhance the readability and understanding of curves.

% Adding a color bar
colorbar;

% Adding a legend
legend('Data 1', 'Data 2');

% Adding a title
title('Real-Time Data Visualization');

# Advanced Applications of MATLAB Dynamic Curve Plotting

### Parallel Plotting of Multiple Curves

In practical applications, it is often necessary to plot multiple curves simultaneously to compare or analyze different data sources. MATLAB provides various methods to achieve parallel plotting of multiple curves.

**Method One: Using the `plot` Function**

The `plot` function can plot multiple datasets simultaneously, with each dataset corresponding to a curve. The syntax is as follows:

plot(x1, y1, 'color1', 'linewidth1', 'linestyle1', ..., xn, yn, 'colorN', 'linewidthN', 'linestyleN')

**Parameter Explanation:**

* `x1`, `y1`, ..., `xn`, `yn`: The datasets to be plotted
* `color1`, ..., `colorN`: The colors of the curves
* `linewidth1`, ..., `linewidthN`: The line widths of the curves
* `linestyle1`, ..., `linestyleN`: The line styles of the curves

**Code Block:**

% Defining data
x1 = 1:10;
y1 = rand(1, 10);
x2 = 1:10;
y2 = rand(1, 10);

% Plotting multiple curves
figure;
plot(x1, y1, 'b', 'LineWidth', 2, 'LineStyle', '-');
hold on;
plot(x2, y2, 'r', 'LineWidth', 1, 'LineStyle', '--');
hold off;

% Adding a legend
legend('Curve 1', 'Curve 2');

**Logical Analysis:**

* The `plot` function is used to draw two curves simultaneously, represented by blue and red.
* The line widths and styles of the curves are set.
* `hold on` and `hold off` are used to control the locking and unlocking of the plotting area to achieve the superimposed drawing of multiple curves.
* A legend is added to distinguish between different curves.

**Method Two: Using the `subplot` Function**

The `subplot` function can divide the plotting area into multiple subplots, with each subplot able to plot one or more curves. The syntax is as follows:

subplot(m, n, p)

**Parameter Explanation:**

* `m`: The number of rows in the subplot
* `n`: The number of columns in the subplot
* `p`: The position of the current subplot among all subplots

**Code Block:**

% Defining data
x1 = 1:10;
y1 = rand(1, 10);
x2 = 1:10;
y2 = rand(1, 10);

% Creating subplots
figure;
subplot(1, 2, 1);
plot(x1, y1, 'b', 'LineWidth', 2, 'LineStyle', '-');
title('Curve 1');
subplot(1, 2, 2);
plot(x2, y2, 'r', 'LineWidth', 1, 'LineStyle', '--');
title('Curve 2');

**Logical Analysis:**

* The `subplot` function is used to create a plotting area with two subplots.
* Curve 1 is drawn in the first subplot, and Curve 2 is drawn in the second subplot.
* The line widths, styles, and titles of the curves are set.

### Curve Fitting and Prediction

Curve fitting refers to finding an optimal curve to approximately describe the trend of data based on given data points. MATLAB provides various curve fitting methods, including polynomial fitting, exponential fitting, and logarithmic fitting.

**Method: Using the `fit` Function**

The `fit` function can perform various types of curve fitting on data. The syntax is as follows:

fit(x, y, 'fittype')

**Parameter Explanation:**

* `x`: Independent variable data
* `y`: Dependent variable data
* `fittype`: The type of fitting, such as `'poly1'` (first-degree polynomial) or `'exp1'` (first-order exponential)

**Code Block:**

% Defining data
x = 1:10;
y = rand(1, 10);

% First-degree polynomial fitting
fitresult = fit(x, y, 'poly1');

% Obtaining the fitted curve
fitcurve = fitresult.FittedModel;

% Plotting the original data and the fitted curve
figure;
plot(x, y, 'o');
hold on;
plot(x, fitcurve(x), 'r', 'LineWidth', 2);
hold off;

% Displaying the fitting equation
disp(['Fitting equation: ' char(fitresult.Formula)]);

**Logical Analysis:**

* The `fit` function is used to perform first-degree polynomial fitting on the data.
* The fitted curve is obtained and plotted over the original data.
* The fitting equation is displayed.

### Data Analysis and Visualization

MATLAB provides a rich library of functions that can be used for various data analysis and visualization operations.

**Data Analysis:**

***Statistical Analysis:** Calculating mean, variance, standard deviation, and other statistical indicators.
***Regression Analysis:** Establishing linear and nonlinear regression models to analyze the relationships between data.
***Classification Analysis:** Using machine learning algorithms to classify data.

**Data Visualization:**

***Bar Chart:** Showing the distribution of data across different categories.
***Pie Chart:** Showing the proportion of different parts in the whole.
***Scatter Plot:** Showing the relationship between two variables.
***Heatmap:** Showing the values of elements in a matrix.

**Code Block:**

% Defining data
data = rand(10, 5);

% Statistical Analysis: Calculating the mean
mean_data = mean(data);

% Regression Analysis: Establishing a linear regression model
model = fitlm(data(:, 1), data(:, 2));

% Data Visualization: Plotting a heatmap
figure;
heatmap(data);

**Logical Analysis:**

* Statistical analysis is performed on the data, and the mean is calculated.
* A linear regression model is established to analyze the relationship between two variables.
* A heatmap is used to visualize the data matrix.

# Real-Time Stock Price Monitoring

Real-time stock price monitoring is a typical application of dynamic curve plotting. By real-time acquisition of stock price data and plotting dynamic curves, investors can visually understand stock price trends and make timely trading decisions.

**Steps:**

1. **Data Acquisition:** Using MATLAB's `quandl` toolbox to obtain stock price data. For example, to get real-time price data for Apple stock (AAPL):

% Using the quandl toolbox to get Apple stock data
AAPL_data = quandl('WIKI/AAPL');

2. **Curve Plotting:** Using the `plot` function to plot the stock price curve. For example, to plot the last 5 days of Apple stock prices:

% Get the last 5 days of data
AAPL_data_5d = AAPL_data(end-4:end, :);

% Plot the curve
plot(AAPL_data_5d.Date, AAPL_data_5d.Close);
xlabel('Date');
ylabel('Close Price');
title('Apple Stock Price');

3. **Real-Time Update:** Using the `timer` function to set a timer to update stock price data and curves at regular intervals. For example, update every 5 seconds:

% Set a timer to update every 5 seconds
timerObj = timer('TimerFcn', @update_plot, 'Period', 5, 'ExecutionMode', 'fixedRate');

% Timer callback function
function update_plot(obj, event)
     % Get the latest data
     new_data = quandl('WIKI/AAPL');
     % Update the curve
     plot(new_data.Date, new_data.Close);
     xlabel('Date');
     ylabel('Close Price');
     title('Apple Stock Price');
end

% Start the timer
start(timerObj);

4. **Interactive Operations:** Using the `datacursormode` function to enable data cursors, allowing users to hover over the curve to view the stock price at specific points.

% Enable data cursor
datacursormode on;

With these steps, a dynamic stock price monitoring system can be created to help investors stay informed about real-time stock market developments.