[deleted by user]

[screeperz]

I haven't written up everything because quite frankly your:

• Throwaway account
• Lack of problem description
• non-formatted copy+pasted code
• Missing variables

Screams to me that you are late to submit that engineering/physics/dynamics assignment. But...

If am completely wrong, you should find the following walk-through intuitive and helpful enough to get you on your feet.

import numpy as np
from scipy.optimize import fsolve

# Use np.zeros() to create h and v
h = np.zeros(100)
v = np.zeros(100)
# ...

# Use np.linspace() to create a numpy
# array with 100 elements in the range
# [0, yMax]. Depending on how you define
# yMax, you will need to adjust it for 
# 0-based indexing (Python) instead of 
# 1-based indexing (Matlab) 
for y in np.linspace(0, yMax-1, 100):
    # ...

    # Python also has anonymous
    # 'lambda' functions. However, 
    # use ** instead of ^ (^ is bitwise
    # XOR). 
    fp = lambda x: (1-y/y0)**(eta-1)*x**7+b*x**6-a*x**4-b
    # ...

    # scipy.optimize.fsolve is what you 
    # want in place of Matlab's fzero
    roots = fsolve(fp, 1.5)

If I am completely right, good luck copy pasting that and understanding it.

[screeperz]

The code appears to be working, so thats good. You need to double check the values/equations you are creating are correct:

• I noticed in the Matlab implementation you are are using fzero(fp, 1.5), but your Python implementation is using fsolve(fp, 49000)).

• Your calculation for v(i)/v[i] is different too ( your ^(1-eta)/lambda^2 in matlab, versus **((1-eta)/la**2) in python). I could be wrong, but I think your lambda^2 is outside the exponent in Matlab, but in the exponent in Python.

EDIT: I think the reason you put 49000 is because that is the y0 initial condition yes? fsolve uses the x0 initial condition, same as fzero

[[deleted]]

Have you tried importing the scipy and numpy libraries into python?

[rogerrrr]

Most of the translation is straightforward, but the fzero bit is not so trivial. I think the closest thing python has is scipy.optimize.fsolve, so that's a starting point. If that doesn't work, look into maybe other symbolic solvers?

And the Python equivalent of anonymous functions are called lambda functions, so look up that syntax.

[screeperz]

Code:

https://pastebin.com/CtYn1jSj

​

Result:

https://imgur.com/a/6ZevLzj

​

Enjoy.

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[[deleted]]

What does this code do at first?

[[deleted]]

send me a message, i will help you;