matplotlib doesn't display the correct data-CodePudding

I am new to Python. For some reason when I look at the plot it displays all the data as if Y = 0 but the last one, which is weird since when I ask it to print Y it displays the right values. What am I doing wrong?

import math
import numpy as np
import matplotlib.pyplot as plt

y0=2 # [m]
g=9.81 # [m/s^2]
v=20 # initial speed [m/s]
y_target=1 # [m]
x=35 # [m]
n_iter=50
theta=np.linspace(0,0.5*math.pi,n_iter) # theta input [rad]
Y=np.zeros(n_iter) # y output [m]
for i in range(n_iter):
    Y[i]=math.tan(theta[i])*x-g/(2*(v*math.cos(theta[i]))**2)*x**2 y0

plt.plot(theta,Y)
plt.ylabel('y [m]')
plt.xlabel('theta [rad]')
plt.ylim(top=max(Y),bottom=min(Y))
plt.show()

CodePudding user response：

The problem is that the function blows up a bit as theta approaches π/2. Notice the little 1e33 at the top of the y-axis in the plot: the scale of that axis is huge, because the last value of y is essentially minus infinity (because of dividing by almost zero). If you change the limits of the y-axis, e.g. to (-1000, 1000), the plot looks correct.

But I can't resist helping you with something you didn't ask for help on... You are not using NumPy correctly. NumPy gives you two things: n-dimensional arrays as a data structure, and fast, optimized code for 'vectorized' computing with those arrays. In essence, you never need a loop in NumPy — you just compute with everything at once. Try doing 10 * np.array([1, 2, 3]) and you will get the idea.

So I would write your code like this:

import numpy as np
import matplotlib.pyplot as plt

# Problem parameters.
y0 = 2       # [m]
g = 9.81     # [m/s^2]
v = 20       # initial speed [m/s]
x = 35       # [m]

# Make theta [rad].
steps = 50
theta = np.linspace(0, 0.5*np.pi, steps)

# Compute y.
y = np.tan(theta) * x - g / (2 * (v * np.cos(theta))**2) * x**2   y0

# Plot.
plt.plot(theta, y)
plt.ylabel('y [m]')
plt.xlabel('theta [rad]')
plt.ylim(-1000, 1000)
plt.show()

Notice that there's no loop — you just use the vector theta as if it were a scalar. And the math library (which can't handle NumPy's arrays, only scalars) is not needed at all when you're using NumPy.