Executing Python script in MATLAB?

Question

Abhishek Sharma 2021-8-7

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/894267-executing-python-script-in-matlab

编辑： Abhishek Sharma 2021-8-9

采纳的回答： Yongjian Feng

Is there any way to run a full python script ? I am not looking to just call a function written in python.

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Answer 1

Yongjian Feng 2021-8-7

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https://ww2.mathworks.cn/matlabcentral/answers/894267-executing-python-script-in-matlab#answer_762912

What is the difference between a full python script and a function written in python? They are the same in my opinion.

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Abhishek Sharma 2021-8-7

import pandas as pd

import random

import numpy as np

import matplotlib.pyplot as plt

from scipy import io

import pickle as pkl

import keras

from keras.models import Sequential, Model, load_model

from keras.layers import LSTM, Dense, RepeatVector, TimeDistributed, Input, BatchNormalization, \

multiply, concatenate, Flatten, Activation, dot

from keras.optimizers import Adam

from keras.utils import plot_model

# from keras.callbacks import EarlyStopping

import pydot as pyd

from keras.utils.vis_utils import plot_model, model_to_dot

keras.utils.vis_utils.pydot = pyd

import numpy as np

import math

import tensorflow as tf

from pandas import DataFrame

from pandas import concat

from pandas import read_csv

from keras.models import Sequential

from keras.layers import Dense, TimeDistributed

from keras.layers import LSTM

from sklearn.preprocessing import MinMaxScaler

from sklearn.metrics import mean_squared_error

from tensorflow.python.keras.callbacks import TensorBoard

import datetime

keras=tf.keras

import scipy.io

from scipy import io

# Get x values of the sine wave

time= np.arange(0, 200, 0.01);

mat = scipy.io.loadmat('y.mat')

amplitude=mat['Y']

# Amplitude of the sine wave is sine of a variable like time

# amplitude= time*0.002+0.02

# amplitude=np.sin(2*math.pi*time)+np.sin(3*2*math.pi*time)+np.sin(5* 2*math.pi*time)

# noise = np. random. normal(0, .2, amplitude_. shape)

# amplitude=amplitude_+noise

# amplitude = scipy.io.loadmat('data.mat')

# amplitude=amplitude['data']

amplitude=amplitude/np.max(amplitude)

df=pd.DataFrame(amplitude)

df_train=amplitude[0:12000]

df_val=amplitude[12000:15000]

df_test=amplitude[15000:20000]

df_train=pd.DataFrame(df_train)

df_val=pd.DataFrame(df_val)

df_test=pd.DataFrame(df_test)

def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):

n_vars = 1 if type(data) is list else data.shape[1]

df = DataFrame(data)

cols, names = list(), list()

# input sequence (t-n, ... t-1)

for i in range(n_in, 0, -1):

cols.append(df.shift(i))

names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]

# forecast sequence (t, t+1, ... t+n)

for i in range(0, n_out):

cols.append(df.shift(-i))

if i == 0:

names += [('var%d(t)' % (j+1)) for j in range(n_vars)]

else:

names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]

# put it all together

agg = concat(cols, axis=1)

agg.columns = names

# drop rows with NaN values

if dropnan:

agg.dropna(inplace=True)

return agg

n_in=100

gap=100 # number of steps after which forecast is needed

n_out=100+gap # horizon of forecast

reframed_train=series_to_supervised(df_train,n_in,n_out)

reframed_val=series_to_supervised(df_val,n_in,n_out)

reframed_test=series_to_supervised(df_test,n_in,n_out)

# reframed_train=reframed_train.drop(['var(t)])

for i in range(1,gap):

reframed_train=reframed_train.drop(['var1(t+%s)' %(i)],axis=1)

reframed_train=reframed_train.drop(['var1(t)'],axis=1)

# for i in range(1,n_out):

# reframed_train=reframed_train.drop(['var2(t+%s)' %(i)],axis=1)

# reframed_train=reframed_train.drop(['var2(t)'],axis=1)

for i in range(1,gap):

reframed_val=reframed_val.drop(['var1(t+%s)' %(i)],axis=1)

reframed_val=reframed_val.drop(['var1(t)'],axis=1)

# for i in range(1,n_out):

# reframed_val=reframed_val.drop(['var2(t+%s)' %(i)],axis=1)

# reframed_val=reframed_val.drop(['var2(t)'],axis=1)

for i in range(1,gap):

reframed_test=reframed_test.drop(['var1(t+%s)' %(i)],axis=1)

reframed_test=reframed_test.drop(['var1(t)'],axis=1)

# for i in range(1,n_out):

# reframed_test=reframed_test.drop(['var2(t+%s)' %(i)],axis=1)

# reframed_test=reframed_test.drop(['var2(t)'],axis=1)

reframed_train=reframed_train.values

reframed_val=reframed_val.values

reframed_test=reframed_test.values

train_X=reframed_train[:,0:(df.shape[1]*n_in)]

train_Y=reframed_train[:,df.shape[1]*n_in:(df.shape[1]*n_in)+df.shape[1]*n_out]

val_X=reframed_val[:,0:(df.shape[1]*n_in)]

val_Y=reframed_val[:,df.shape[1]*n_in:(df.shape[1]*n_in)+df.shape[1]*n_out]

test_X=reframed_test[:,0:(df.shape[1]*n_in)]

test_Y=reframed_test[:,df.shape[1]*n_in:(df.shape[1]*n_in)+df.shape[1]*n_out]

train_X = train_X.reshape((train_X.shape[0],n_in, df.shape[1]))

train_Y = train_Y.reshape((train_Y.shape[0],n_out-gap,1))

val_X = val_X.reshape((val_X.shape[0],n_in, df.shape[1]))

val_Y = val_Y.reshape((val_Y.shape[0],n_out-gap,1))

test_X=test_X.reshape((test_X.shape[0],n_in, df.shape[1]))

test_Y = test_Y.reshape((test_Y.shape[0],n_out-gap,1))

# def create_model():

# return tf.keras.models.Sequential([

# tf.keras.layers.Bidirectional(LSTM(12 8,return_sequences=False,stateful=False,batch_input_shape=(None,n_in,1))),

# # tf.keras.layers.Bidirectional(LSTM(50,return_sequences=True)),

# tf.keras.layers.Dense(n_out-gap)])

# #model.add(Dense(1))

# model = create_model()

# model.compile(loss=keras.losses.Huber(), optimizer='adam',metrics=["mae"])

# early_stopping = keras.callbacks.EarlyStopping(patience=10)

# history=model.fit(train_X, train_Y,validation_data=(val_X, val_Y),batch_size=32,epochs=1,callbacks=[early_stopping],shuffle=True)

# history=model.fit(train_X, train_Y,validation_data=(val_X, val_Y),batch_size=1,epochs=50,shuffle=False)

# # model.save('sinus.h5')

# m=model.predict(test_X)

# m = m.reshape((m.shape[0],n_out-gap,1))

# io.savemat("forecast_lin.mat",{"forecast_lin": m})

# io.savemat("true.mat",{"true": test_Y})

# error=np.abs(test_Y-m)

# mape=np.mean(np.abs((test_Y - m) / m)) * 100

# print('MAPE is: %.2f percent' % (mape))

Abhishek Sharma 2021-8-9

编辑：Abhishek Sharma 2021-8-9

I think it's not able to import the modules.

Yongjian Feng 2021-8-9

Is pandas installed properly in python? Can you use python to import it without problems?
Figure out where from it is imported, then inside matlab, call setenv (https://www.mathworks.com/help/matlab/ref/setenv.html) to set PYTHONPATH to add that location to it.

If you don't use virtualenv, this can be done easily.

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