Monday 8 April 2024

Plant Leaf using Python

 

import numpy as np

import matplotlib.pyplot as plt

t = np.linspace(0, 39*np.pi/2, 1000)

x = t * np.cos(t)**3

y = 9*t * np.sqrt(np.abs(np.cos(t))) + t * np.sin(0.2*t) * np.cos(4*t)

plt.plot(x, y, c="green")

plt.show()

#clcoding.com

Explanation: 

This code snippet is written in Python and utilizes two popular libraries for numerical computing and visualization: NumPy and Matplotlib.

Here's a breakdown of what each part of the code does:

import numpy as np: This line imports the NumPy library and allows you to use its functionalities in your code. NumPy is a powerful library for numerical computations in Python, providing support for arrays, matrices, and mathematical functions.

import matplotlib.pyplot as plt: This line imports the pyplot module from the Matplotlib library, which is used for creating static, animated, and interactive visualizations in Python. It's typically imported with the alias plt for convenience.

t = np.linspace(0, 39*np.pi/2, 1000): This line generates an array t of 1000 equally spaced values ranging from 0 to 
39
2
2
39ฯ€
 . np.linspace() is a NumPy function that generates linearly spaced numbers.

x = t * np.cos(t)**3: This line computes the x-coordinates of the points on the plot. It utilizes NumPy's array operations to compute the expression 
×
cos
(
)
3
t×cos(t) 
3
 .

y = 9*t * np.sqrt(np.abs(np.cos(t))) + t * np.sin(0.2*t) * np.cos(4*t): This line computes the y-coordinates of the points on the plot. It's a more complex expression involving trigonometric functions and mathematical operations.

plt.plot(x, y, c="green"): This line plots the x and y coordinates as a line plot. The c="green" argument specifies the color of the line as green.

plt.show(): This line displays the plot on the screen. It's necessary to call this function to actually see the plot.

Overall, this code generates a plot of a parametric curve defined by the equations for x and y. The resulting plot will depict the curve in green.



import numpy as np
import matplotlib.pyplot as plt

# Define the parameter t
t = np.linspace(0, 39*np.pi/2, 1000)

# Define the equations for x and y
x = t * np.cos(t)**3
y = 9*t * np.sqrt(np.abs(np.cos(t))) + t * np.sin(0.2*t) * np.cos(4*t)

# Define the segment indices and corresponding colors
segments = [
    (0, 200, 'orange'),  # Green segment
    (200, 600, 'magenta'),   # Red segment
    (600, 1000, 'red')  # Orange segment
]

# Plot each segment separately with the corresponding color
for start, end, color in segments:
    plt.plot(x[start:end], y[start:end], c=color)

plt.show()
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