How to convert python code to Matlab?

Is there way to convert this python code to matlab code?

how can i convert python code to matlab???

this is the code that I want to convert:

import os
os.environ("KMP_DUPLICATE_LIB_OK") = "TRUE";  %%aggiungo una variabile ambiente
from sklearn.cluster import estimate_bandwidth
from sklearn import metrics
def estimate_bandwidth_meanshift(features, perc, quantile=0.5):
    print('Start estimating bandwidth -------------------------------------')
    
    bandwidth = estimate_bandwidth(features, quantile=quantile, n_samples = int(features.shape(0)*perc/100))
    
    print('End estimating bandwidth ---------------------------------------')
    
    return bandwidth
def getNMI(prediction, gt):
    return metrics.normalized_mutual_info_score(gt, prediction)
def getARI(prediction, gt):
    return metrics.adjusted_rand_score(gt, prediction)

[Rik]

There is no automated conversion possible.

You have two options:

1. Look up the meaning of each function and implement the working of the code in Matlab.
2. Run the python code from within Matlab.

Which did you try?

[filippo gistri]

I tried to run python on matlab, but it didn't work.

If i run, matlab return this error:

Error using python_utility><module>

Error in <frozen importlib>_call_with_frames_removed (line 241)

Error in <frozen importlib>exec_module (line 883)

Error in <frozen importlib>_load_unlocked (line 688)

Error in <frozen importlib>_find_and_load_unlocked (line 1006)

Error in <frozen importlib>_find_and_load (line 1027)

Error in <frozen importlib>_gcd_import (line 1050)

Error in __init__>import_module (line 126)

[Rik]

I don't work with the Python link enough to understand this error without seeing the code you tried.

Answer 1

[Aditya Jha]

Hi!

There is no direct way to convert python code to MATLAB.

There are two approaches:

• You have to directly write the code from scratch.
• You can use MATLB API to call python script in MATLAB
• https://www.mathworks.com/help/matlab/call-python-libraries.html
• As of MATLAB 8.4(R2014b) you can call Python directly from MATLAB

Refer to the following MATLAB answers post with similar issue:

https://www.mathworks.com/matlabcentral/answers/416129-how-to-convert-python-code-into-matlab

Answer 2

[rushabh rupani]

Converting Python code to Matlab can be a complex process, as the two languages have different syntax and libraries.

You can use the Py2Mat library to accomplish your desired result.

Answer 3

[Lekkalapudi Chandini]

% Plot the displacement versus time
plot(t, x(:, 1))
xlabel('Time (s)')
ylabel('Displacement (m)')
title('Displacement of Mechanical System')

Answer 4

[Anoy Chowdhury]

Only some commands like

py.list({'This','is a','list'}) 

are recommendable to use from python. In the other hand, you can create a full funcion in python script / function and call it with:

pyrun('my_python_callback')

However, trying to "convert" a python script line by line to MATLAB is pointless. To give you a context, is equivalent to try to convert to python some script which includes:

%...
sys = armax(tt2,[na nb nc nk])
%...

Since armax belongs to a toolbox (System Identification) it would be extremely complicated and unpractical.

If you want to use a python library as sklearn.cluster, stay in python, the only reason to import to MATLAB should be importing a python environment to interact with an MATLAB , for example an MATLAB AI agent:

classdef mountain_car_1 < rl.env.MATLABEnvironment
  properties
    open_env = py.gym.make('MountainCar-v0'); %% IMPORT PYTHON OPEN AI ENVIRONMENT
  end
  methods
    function this = mountain_car_1()
      ObservationInfo = rlNumericSpec([2 1]);
      ObservationInfo.Name = 'MountainCar Descreet';
      ObservationInfo.Description = 'Position, Velocity';
      ActionInfo = rlFiniteSetSpec([0 1 2]);
      ActionInfo.Name = 'Acceleration direction';
      this = [e-mail](ObservationInfo,ActionInfo);
    end
    function [Observation,Reward,IsDone,LoggedSignals] = step(this,Action)
      result = cell(this.open_env.step(int16(Action))); 
      Observation = double(result{1})'; 
      Reward = double(result{2});
      IsDone = double(result{3});
      LoggedSignals = [];
      if (Observation(1)>=0.4)
            Reward = 0;
            IsDone = 1;
      end
    end
    function InitialObservation = reset(this)
      result = this.open_env.reset();
      InitialObservation = double(result)'; 
    end
  end
end 

In the previous example, if you had installed Python 3.10, and OpenAIGym, you can import the desired environment to MATLAB and interact both with the used and a potencial AI Bot.

Answer 5

[Yerisn]

import numpy as np

# Paso 1: Definir parámetros geométricos y mecánicos

E = 200000 # Módulo elástico en MPa

alpha = 12e-6 # Coeficiente de dilatación térmica en 1/°C

delta_T = -20 # Diferencia de temperatura por enfriamiento en °C

A1 = 6000 # Área de la barra 1 en mm^2

A2 = 5000 # Área de la barra 2 en mm^2

A3 = 4000 # Área de la barra 3 en mm^2

L1 = 6000 # Longitud de la barra 1 en mm

L2 = 8000 # Longitud de la barra 2 en mm

L3 = 5000 # Longitud de la barra 3 en mm

angles = [-np.pi/2, 5 * np.pi / 6, np.pi / 4] # Ángulos en radianes

# Paso 2: Definir la tabla de conectividades

connectivity = np.array([[1, 2], [1, 4], [1, 3]]) # Nodos

dof_connectivity = np.array([[0, 1, 2, 3], [0, 1, 6, 7], [0, 1, 4, 5]]) # Grados de libertad

print("Tabla de conectividades")

print(connectivity.reshape((3, 2)))

print(dof_connectivity.reshape((3, 4)))

# Paso 3: Definir las matrices de rigidez elementales referidas a los sistemas de referencia locales

k1_local = (E * A1 / L1) * np.array([[1, -1], [-1, 1]])

k2_local = (E * A2 / L2) * np.array([[1, -1], [-1, 1]])

k3_local = (E * A3 / L3) * np.array([[1, -1], [-1, 1]])

print("Matrices de rigidez elementales referidas a los sistemas de referencia locales")

print(k1_local)

print(k2_local)

print(k3_local)

# Paso 4: Definir los vectores de cargas elementales (por temperatura y rigidez) referida a los sistemas de referencia locales

f_T1 = np.array([-alpha * delta_T * E * A1, alpha * delta_T * E * A1])

f_T2 = np.array([-alpha * delta_T * E * A2, alpha * delta_T * E * A2])

f_T3 = np.array([-alpha * delta_T * E * A3, alpha * delta_T * E * A3])

print("Vectores de cargas elementales")

print(f_T1)

print(f_T2)

print(f_T3)

# Paso 5: Definir las matrices de rotación de cada barra

def rotation_matrix(angle):

c = np.cos(angle)

s = np.sin(angle)

return np.array([[c, s, 0, 0], [0, 0, c, s]])

R1 = rotation_matrix(angles[0]) # Barra 1 (ángulo de -π/2 radianes)

R2 = rotation_matrix(angles[1]) # Barra 2 (ángulo de 5π/6 radianes)

R3 = rotation_matrix(angles[2]) # Barra 3 (ángulo de π/4 radianes)

print("Matrices de rotación de cada barra")

print(R1)

print(R2)

print(R3)

# Paso 6: Transformar las matrices de rigidez del sistema de referencia local al sistema de referencia global

k1_global = R1.T @ k1_local @ R1

k2_global = R2.T @ k2_local @ R2

k3_global = R3.T @ k3_local @ R3

print("Transformación de las matrices de rigidez del sistema de referencia local al sistema de referencia global")

print(k1_global)

print(k2_global)

print(k3_global)

# Paso 7: Transformar los vectores de carga del sistema de referencia local al sistema de referencia global

F_T1_global = R1.T @ f_T1

F_T2_global = R2.T @ f_T2

F_T3_global = R3.T @ f_T3

print("Transformación de los vectores de carga del sistema de referencia local al sistema de referencia global")

print(F_T1_global)

print(F_T2_global)

print(F_T3_global)

# Paso 8: Ensamblar la matriz de rigidez global (8x8)

K_global = np.zeros((8, 8))

# Contribución de la barra 1 (nodo 1 a nodo 2)

K_global[np.ix_([0, 1, 2, 3], [0, 1, 2, 3])] += k1_global

# Contribución de la barra 2 (nodo 1 a nodo 4)

K_global[np.ix_([0, 1, 6, 7], [0, 1, 6, 7])] += k2_global

# Contribución de la barra 3 (nodo 1 a nodo 3)

K_global[np.ix_([0, 1, 4, 5], [0, 1, 4, 5])] += k3_global

print("Matriz de rigidez global")

print(K_global)

# Paso 9: Ensamblar el vector de carga global

F_global = np.zeros(8)

F_global[0:2] += F_T1_global[:2]

F_global[0:2] += F_T2_global[:2]

F_global[0:2] += F_T3_global[:2]

print("Vector de carga global")

print(F_global)

# Condiciones de contorno: nodos 2, 3 y 4 son empotrados (desplazamientos = 0)

fixed_dofs = [2, 3, 4, 5, 6, 7]

free_dofs = [0, 1]

# Reducción de la matriz de rigidez y el vector de fuerzas

K_reduced = K_global[np.ix_(free_dofs, free_dofs)]

F_reduced = F_global[free_dofs]

# Paso 10: Resolver las incógnitas

# a) Desplazamiento del nodo central 1

displacements = np.linalg.solve(K_reduced, F_reduced)

# Vector de desplazamientos completo

displacement_vector = np.zeros(8)

displacement_vector[free_dofs] = displacements

# b) Calcular las reacciones de vínculo en los empotramientos

reactions = K_global @ displacement_vector - F_global

# c) Calcular deformaciones y tensiones en cada barra

deformation_1 = (displacement_vector[1] - displacement_vector[0]) / L1

deformation_2 = (displacement_vector[7] - displacement_vector[6]) / L2

deformation_3 = (displacement_vector[5] - displacement_vector[4]) / L3

stress_1 = E * deformation_1

stress_2 = E * deformation_2

stress_3 = E * deformation_3

# Resultados en formato de matrices

print("Desplazamiento del nodo central 1 (x, y):")

print(displacements.reshape((2, 1)))

print("Reacciones de vínculo en los empotramientos:")

print(reactions.reshape((8, 1)))

print("Deformaciones en cada barra (1, 2, 3):")

print(np.array([deformation_1, deformation_2, deformation_3]).reshape((3, 1)))

print("Tensiones en cada barra (1, 2, 3):")

print(np.array([stress_1, stress_2, stress_3]).reshape((3, 1)))

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