How to convert Python code into matlab

I have a Python code.how can I convert it into matlab code or is there any option of executing the same using matlab

[suresh]

%% Fractional Leaky Integrate-and-Fire Model
% Model files for the paper
% Wondimu Teka,  Toma M.Marinov, Fidel Santamaria (2014) Neuronal Spike Timing Adaptation Described with a Fractional Leaky Integrate-and-Fire Model.
% PLoS Comput Biol 10(3): e1003526. doi:10.1371/journal.pcbi.1003526

clear
rseed=2342;
Ncells=1; %number of cells,  do not change this value, this code is not for network, it is only for one cell.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%   parameters  values that can be varied.
dt=0.1; %ms  time step. This value is used for  all the data.
t=0:dt:1000; %ms    mostly is  up to  5000 ms (5sec)  or  10000 ms (10 sec) is used.
% alpha= ??; %  the fractional exponent (order)  it is  varied  below using a for loop

Rm=40; %Mohms
taum=20*ones([1 Ncells]); %ms membrane time constant
Cm=0.5; %nF  % this is not used since taum and Rm are used and note taum = Cm*Rm
refrac=8*ones([1 Ncells]); %ms absolute refarctory period
v0= -70*ones([1 Ncells]);  % mV  initial value
% initial voltage was varied: used values are v0= -70, v0= -75 v0= -58, v0= -45,
vrest= -70;     % mV  the resting  potential which is used for reset too.
vth= vrest + 20*ones([1 Ncells]); %  is  -50  mV,   threshold value of membrane voltage
vpeak = vrest + 100*ones([1 Ncells]); % is  30 mV,   peak value of he voltage at spike
Iinjamplitude= 3;  % nA  injected current amplitude, see Iinj  for the whole injected current
%  for the subthershold  use Iinjamplitude= 0.3;
Namp=0; %noise amplitude, to add white Gaussian noise with this  standard devation Namp, which is noise amplitude
%  This value should be changed only for figure 11,
% Namp=1  %  For only figure 11,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%  used for most of the simulations
Iinj=Iinjamplitude*ones(length(t),Ncells)+ Namp*(randn(length(t),Ncells));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% for the sinusoidal simulation with  non constant freqency, use the following  ZAP current
%  f=0.1*(2./(1+exp(-t./1500))-1).^3;  % frequency  in cycle  per ms. since time is in ms; the freqency increases from 0  Hz to  100 Hz
%  Iinj=0.3*sin((2*pi).*f.*t)';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%  for figure 10 use the following current instead of the above
% for the sinusoidal simulation with constant freqency, use the following
%f = 0.003  %  this is in  cycle per ms, it is the same is     3 Hz
%Iinj= 2 +2*sin((0:dt:t(end)).*2*pi*f)';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% names for parameters to pass them to the  main  function: runNetworkderivative
NetProp.Ncells=Ncells;
NetProp.Rm=Rm; %Mohms
NetProp.TauM=taum;
NetProp.Cm=Cm; %nF  % this is not used since taum and Rm are used.
NetProp.Refrac=refrac;
NetProp.vTh=vth;
NetProp.v0=v0;
NetProp.vrest=vrest;
NetProp.vpeak=vpeak;
NetProp.Noise=Namp;
NetProp.dt=dt;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Main integrator
%  Integrating  output for different alpha values using  the fractional derivative
b=1;
for alpha=1:-0.2:0.2
    out(b)=runNetworkderivative(NetProp,Iinj,t,rseed,alpha); % main output
    % Note:This has the following output out.v=v  for voltage; out.sp=sp for spike ; out.Memo2=Memo2 for memery; out.t=t for time;
    alphavalue(b)=alpha
    ISI{b}=diff(out(b).t(logical(out(b).sp(1:end-1))));  %  calculates  ISI in msec
    firingrate{b} = 1000./ISI{b};  %in  Hz, calculates firing rate
    totalspike(b) = sum(out(b).sp); % for toltal number of spikes for  total time
    b= b+1
end

cv=['kbgmr'];
c=1;
for b=1:length(out)  % for  alpha =1.0
    subplot(5,1,c)
    trange=1:1000/dt;
    plot(out(b).t(trange), out(b).v(trange), cv(b), 'Linewidth',1.5)  %
    set(gca, 'FontSize', 14, 'FontName', 'Helvetica')
    set(gca, 'LooseInset', get(gca,'TightInset'))
    ylabel('V (mV)', 'fontsize',14, 'FontName', 'Helvetica');
    legendinfo2= sprintf('\\alpha= %0.1f\n', alphavalue(b));
    c=c+1;
    axis([-1  1000  -75   35])
    legend(legendinfo2,'Location','SouthWest', 'fontsize',11, 'FontName', 'Helvetica')
    clear legendinfo2;
end
xlabel('Time (ms)', 'fontsize',14, 'FontName', 'Helvetica');

[Rik]

@Suresh, now that your code is properly formatted, can you explain what this has to do with this question?

[nagaraja kumar]

import cv2
import numpy as np




class ContrastMeasures():


    def __init__(self):
        pass


    def fm(self, img, fm_name, window_size=3, threshold=7):
        """
        applies focus measure fm_name to input image img
        fm_name must be one of the following:
        'SML', 'CMSL', 'GLV', 'TENENGRAD1', 'JAEHNE'"""
        if fm_name == 'SML':
            return self.SML(img, window_size, threshold)
        elif fm_name == 'CMSL':
            return self.CMSL(img, window_size)
        elif fm_name == 'GLV':
            return self.GLV(img, window_size)
        elif fm_name == 'TENENGRAD1':
            return self.tenengrad1(img, window_size, threshold)
        elif fm_name == 'JAEHNE':
            return self.jaehne(img, window_size)


    def CMSL(self, img, window):
        """
        Contrast Measure based on squared Laplacian according to
        'Robust Automatic Focus Algorithm for Low Contrast Images
        Using a New Contrast Measure'
        by Xu et Al. doi:10.3390/s110908281
        window: window size= window X window"""
        ky1 = np.array(([0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]))
        ky2 = np.array(([0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]))
        kx1 = np.array(([0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]))
        kx2 = np.array(([0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]))
        g_img = abs(cv2.filter2D(img, cv2.CV_32F, kx1)) + \
                abs(cv2.filter2D(img, cv2.CV_32F, ky1)) + \
                abs(cv2.filter2D(img, cv2.CV_32F, kx2)) + \
                abs(cv2.filter2D(img, cv2.CV_32F, ky2))
        return cv2.boxFilter(
                                g_img * g_img,
                                -1,
                                (window, window),
                                normalize=True)


    def SML(self, img_in, window_size, threshold):
        """
        Sum of modified Laplacian according to
        'Depth Map Estimation Using Multi-Focus Imaging'
        by Mendapara
        """
        # kernels in x- and y -direction for Laplacian
        ky = np.array(([0.0, -1.0, 0.0], [0.0, 2.0, 0.0], [0.0, -1.0, 0.0]))
        kx = np.array(([0.0, 0.0, 0.0], [-1.0, 2.0, -1.0], [0.0, 0.0, 0.0]))
        # add absoulte of image convolved with kx to absolute
        # of image convolved with ky (modified laplacian)
        ml_img = abs(cv2.filter2D(img_in, cv2.CV_32F, kx)) + \
                abs(cv2.filter2D(img_in, cv2.CV_32F, ky))
        # sum up all values that are bigger than threshold in window
        ret, img_t = cv2.threshold(ml_img, threshold, 0.0, cv2.THRESH_TOZERO)
        return cv2.boxFilter(
                                img_t,
                                -1,
                                (window_size, window_size),
                                normalize=False)


    def GLV(self, img, window_size=3):
        """
        Gray Level Variance according to
        'Depth Map Estimation Using Multi-Focus Imaging'
        by Mendapara
        """
        # calculate mean for each window
        mean = cv2.boxFilter(
                                img,
                                cv2.CV_32F,
                                (window_size, window_size),
                                normalize=True)
        # return variance=(img[x,y]-mean[x,y])^2
        return (img - mean)**2.0


    def tenengrad1(self, img, window_size, threshold):
        """
        Tenengrad2b: squared gradient absolute thresholded and 
        summed up in each window
        according to
        'Autofocusing Algorithm Selection in Computer Microscopy'
        by Sun et Al.
        """
        # calculate gradient magnitude:
        S = cv2.Sobel(img, cv2.CV_32F, 1, 0, 3)**2.0 + \
            cv2.Sobel(img, cv2.CV_32F, 0, 1, 3)**2.0
        # threshold image
        ret, dst = cv2.threshold(S, threshold, 0.0, cv2.THRESH_TOZERO)
        # return thresholded image summed up in each window:
        return cv2.boxFilter(
                                dst,
                                -1,
                                (window_size, window_size),
                                normalize=False)


    def jaehne(self, img, window_size):
        """Only implemented for window_size 3 or 5 according to
        'Entwicklung einer fokusbasierenden Hoehenmessung mit
        dem "Depth from Focus"-Verfahren' by Dunck
        """
        if window_size == 3:
            kernel = np.array([
                            [1.0, 2.0, 1.0],
                            [2.0, 4.0, 2.0],
                            [1.0, 2.0, 1.0]])
            sum = 16
        elif window_size == 5:
            kernel = np.array([
                            [1.0, 4.0, 6.0, 4.0, 1.0],
                            [4.0, 16.0, 24.0, 16.0, 4.0],
                            [6.0, 24.0, 36.0, 24.0, 6.0],
                            [4.0, 16.0, 24.0, 16.0, 4.0],
                            [1.0, 4.0, 6.0, 4.0, 1.0]])
            sum = 256
        img_t = (img - cv2.filter2D(img, cv2.CV_32F, kernel) / sum)**2
        return cv2.filter2D(img_t, -1, kernel) / sum

• © 2021 GitHub, Inc.

[wafa derouaz]

Vombarelli Neha Please, didi you find the solution to this problem?

[Evens MONTILUS]

import numpy as np
impot matplotlib.pyplot as plt
line=np.linspace(-5,5,1000)
#plt.plot(line,np.tanh(line), label=''tanh'')
#plt.plot(line,np.maximum(line,0), label=''sigmoid'')
plt.plot(line,1/(1+np.exp(-line)),label=''sigmoid'')
plt.legend(loc=''best'')
plt.xlabel(''z'')
#plt.ylabel(''Relu(x),tanh(x), sigmoid(x)'')
plt.ylabel(''sigmoid(z)'')

Answer 1

[Rik]

See this, or with system('python python_script.py');

This was the top result for my Google search: link

[Camilo Romero]

The code above only executes the python code in matlab but if you want to use all matlab capabilities such as the debug part (debugging python code in matlab) the system function is not an option.

[Walter Roberson]

https://www.mathworks.com/products/matlab/matlab-and-python.html

[Mohammed Ouallal]

I believe Vombarelli Neha said that "he or she" wants to "convert" not "call" !

[Rik]

Read on: "I have a Python code.how can I convert it into matlab code or is there any option of executing the same using matlab".

Anyway, a strict convert is theoretically possible (since both languages are Turing-complete), although this conversion is probably either not automatic, or not effecicient. You should either re-write it completely in Matlab, or run it as Python code. Unless you find/write a good Python2Matlab function, I know there are surprisingly good converters sometimes.

[Evens MONTILUS]

import numpy as np

impot matplotlib.pyplot as plt

line=np.linspace(-5,5,1000)

#plt.plot(line,np.tanh(line), label=''tanh'')

#plt.plot(line,np.maximum(line,0), label=''sigmoid'')

plt.plot(line,1/(1+np.exp(-line)),label=''sigmoid'')

plt.legend(loc=''best'')

plt.xlabel(''z'')

#plt.ylabel(''Relu(x),tanh(x), sigmoid(x)'')

plt.ylabel(''sigmoid(z)'')

Answer 2

[Gautam Takoo]

I have a Python code.how can I convert it into matlab code or is there any option of executing the same using matlab

import cv2

from PIL import Image

import numpy as np

from glob import glob

import os

def main():

# path of the folder containing the raw images

inPath =("Z://Randomimages")

# path of the folder that will contain the modified image

outPath =("Z:/normaltogray")

for files in os.walk(inPath):

for imagePath in os.listdir(inPath):

if not imagePath.endswith(".jpg"):

print("{} file is not an expected file".format(imagePath))

continue

inputPath = os.path.join(inPath, imagePath)

img = np.array(Image.open(inputPath))

if imagePath.startswith('T1_E1'):

roi = img[1360:1470,850:2700]

elif imagePath.startswith('T1_E2'):

roi= img[1370:1450,920:2770]

gray=cv2.cvtColor(roi,cv2.COLOR_BGR2GRAY)

fullOutPath = os.path.join(outPath,imagePath)

cv2.imwrite(fullOutPath,gray)

print(fullOutPath)

cv2.waitKey(0)

cv2.destroyAllWindows()

# Driver Function

if __name__ == '__main__':

main()

#

[Rik]

If you didn't bother reading my answer, why should I bother responding to your question?

[Walter Roberson]

http://matlab.wikia.com/wiki/FAQ#How_can_I_process_a_sequence_of_files.3F

together with imcrop() and rgb2gray()

Answer 3

[Yalew Mekonnen]

https://in.mathworks.com/search.html?c%5B%5D=entire_site&q=Using%20MATLAB%20with%20python&page=1

This may help you. There are a number of ducoments and videos related to using MATLAB with Python.

Answer 4

[ASHOK KUMAR MEENA]

def Lagrange(x, y, n, xx):

sum = 0

for i in range(0, n + 1):

product = y[i]

for j in range(0, n + 1):

if (i != j):

product = product * (xx - x[j]) / (x[i] - x[j])

sum += product

return sum

def Trapezoidal(h, n, f):

sum = f[0]

for i in range (1, n):

sum = sum + 2 * f[i]

sum = sum + f[n]

ans = h * sum / 2

return ans