[python, signal, normal] How to generate a normal distribution data

How to generate a normal distribution data

井民全, Jing, mqjing@gmail.com

Example 1

Create a data array with 30 samples in which the properties of  mean is 80 and the standard deviation is 1.

Code

mean = 80 std = 1 N = 30 np.random.seed(1) arrA= np.random.normal(mean, std, N)

Example 2

Create a 100 samples data array with mixed two distribution in which the first distribution has 30 samples with properties of means as 40 with standard deviation 1 and the second distribution has 70 samples with means as 60 and the standard deviation as 10.

Code

np.random.seed(1)     N = 100 # total samples     # normal distribution list. mean = 40, std= 1, number of samples = 30%     arrA = np.random.normal(40, 1, int(0.3 * N))          # normal distribution list. mean = 60, std= 10, number of samples = 70%     arrB = np.random.normal(60, scale=10, size=int(0.7 * N))      X = np.concatenate((arrA, arrB))         fig, axes = plt.subplots(nrows=2, sharex=True, gridspec_kw={"height_ratios": [1,1]})     fig.suptitle('Fig. Test of Normal Distribution and K-means.')     fig.canvas.set_window_title('Test of Normal Distribution')          # show all samples     i = 0     axes[i].plot([x for x in range(0, X.size)], X, "tab:red", label="X")     axes[i].set_title("(a) Data X with two distributions.")     axes[i].set_ylabel("Value")     axes[i].set_xlabel("Samples")     axes[i].grid()     axes[i].legend()          # show histogram     i = 1     bins = np.linspace(0, N, N)     axes[i].hist(X, bins=bins, fc='#AAAAFF')     axes[i].set_title("(b) Show the frequency histogram of X.")     axes[i].set_ylabel("Histogram")     axes[i].set_xlabel("Values")          plt.tight_layout() # Prvent the figure title overlaps axes label     plt.show()

Result

Reference

1. https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html