Matlab call python script with arguments

Hi, I tried to run a python script in matlab. I used the function system.So fa so good.

The pyhton script uses two input variables. In matlab I can enter one and then it runs without problems but when I try to enter two separate variables I always get an error.

systemCommand = ['python sqd.py ',num2str(a),num2str(omtrek)]

[status, result] = system(systemCommand);

This is the error:

status =

1

result =

Traceback (most recent call last):

File "sqd.py", line 10, in <module>

sys.stdout.write(str(squared(x)))

TypeError: squared() takes exactly 2 arguments (1 given)

Can someone help me?

Thanks!

Accepted Answer

Hi,

when modifying your script to:

import sys

def squared(a,omtrek):

b = (omtrek/2)-a

return b

if __name__ == '__main__':

x = float(sys.argv[1])

y = float(sys.argv[2])

sys.stdout.write(str(squared(x,y)))

and calling it from MATLAB:

>>!C:\Python32\python.exe sqd.py 1 2

it works fine for me. Or in your way:

>> a = 1

>> omtrek = 2

>>systemCommand = ['C:\Python32\python.exe sqd.py ',num2str(a),' ',num2str(omtrek)]

>> system(systemCommand)

0.0

ans =

0

More Answers (7)

Why not using Python Interface?

The MATLAB statement can be as simple as following:

>> b=py.sqd.squared(10,30)

Could you try the above with a space between the two arguments?

systemCommand = ['python sqd.py ',num2str(a),' ',num2str(omtrek)]

This is the python script:

import sys

def squared(a,omtrek):

b = (omtrek/2)-a

return b

if __name__ == '__main__':

x = float(sys.argv[1])

sys.stdout.write(str(squared(x)))

It's very easy code, but this is just a test for a more complex program I would like to run.

In python it works. In Matlab it doesn't. It's true that the code needs two inputs. But my question is how can I give two inputs using matlab?

You are right Friedrich, this works! Thanks!

But by doing this I have another problem. The python code calculates the value of a parameter "b". I need this parameter in matlab for further processing. The correct value appears on my screen but I can't handle it. For example like this:

Can any one help me how to integrate this python code to matlab, i want to run this python program in matlab...

#Load dependencies import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler from matplotlib import* import matplotlib.pyplot as plt from matplotlib.cm import register_cmap from matplotlib.mlab import PCA from sklearn.decomposition import PCA

from scipy import stats #from wpca import PCA from sklearn.decomposition import PCA as sklearnPCA import seaborn

#Load movie names and movie ratings movies = pd.read_csv('movies.csv') ratings = pd.read_csv('ratings.csv') ratings.drop(['timestamp'], axis=1, inplace=True) # def replace_name(x): return movies[movies['movieId']==x].title.values[0] # ratings.movieId = ratings.movieId.map(replace_name) # M = ratings.pivot_table(index=['userId'], columns=['movieId'], values='rating') m = M.shape #posesall = pd.read_csv('FileName_Poses.csv') df1 = M.replace(np.nan, 0, regex=True) X_std = StandardScaler().fit_transform(movies) #Step 2: Covariance Matrix and Eigendecomposition mean_vec = np.mean(X_std, axis=0) cov_mat = (X_std - mean_vec).T.dot((X_std - mean_vec)) / (X_std.shape[0]-1) print('Covariance matrix \n%s' %cov_mat) print('NumPy covariance matrix: \n%s' %np.cov(X_std.T)) #Perform eigendecomposition on covariance matrix cov_mat = np.cov(X_std.T) eig_vals, eig_vecs = np.linalg.eig(cov_mat) print('Eigenvectors \n%s' %eig_vecs) print('\nEigenvalues \n%s' %eig_vals) # Step 3: Selecting Principal Components # Visually confirm that the list is correctly sorted by decreasing eigenvalues eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))] print('Eigenvalues in descending order:') for i in eig_pairs: print(i[0]) pca = PCA(n_components = 93) All_poses_pca = pca.fit_transform(movies) Variance = (pca.explained_variance_ratio_)

#Explained variance pca = PCA().fit(X_std) plt.plot(np.cumsum(pca.explained_variance_ratio_)) plt.title('Scree Plot') plt.xlabel('Number of principal components') plt.ylabel('Cumulative explained variance') plt.show()

It works for me as well, but it is very slow. It takes 0.2 sec to call a sum function from Python

Any ideas on how to speed it up? Where is the bottle neck?

Hello everyone! I have some images read in matlab and I want to process them in python and them return them to matlab for further processing.

Everything with the systemCommand works, however, even though I can import variables from matlab to python, because they are already in the workspace, I cannot return variables (e.g. images) back to matlab, when running the python script. Till now, in order to make it work, I save the python-processed images and then read them again from matlab.

I would like to ask if there is a more efficient way. In other words, in the example of Tom Leblicq, I would like to return the parameter b in matlab for further processing.

Thank you in advance!

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