How can combine 3 matrices into 1 matrice with reversible-approach?

问题

I want to reshape my 24x20 matrices 'A','B','C' which are extracted from text file and are saved before and after normalizing by def normalize() in for-loop through cycles in such way that each cycles would be a row with all elements of 3 matrices side by side like below:

[[A(1,1),B(1,1),C(1,1),A(1,2),B(1,2),C(1,2),...,A(24,20),B(24,20),C(24,20)] #cycle1
 [A(1,1),B(1,1),C(1,1),A(1,2),B(1,2),C(1,2),...,A(24,20),B(24,20),C(24,20)] #cycle2
 [A(1,1),B(1,1),C(1,1),A(1,2),B(1,2),C(1,2),...,A(24,20),B(24,20),C(24,20)]] #cycle3

So far based on @odyse suggestion I used following snippet in the end of for-loop:

for cycle in range(cycles):
dff = pd.DataFrame({'A_norm':A_norm[cycle] , 'B_norm': B_norm[cycle] , 'C_norm': C_norm[cycle] } , index=[0])
D = dff.as_matrix().ravel()
if cycle == 0:
    Results = np.array(D)
else:
    Results = np.vstack((Results, D2))
np.savetxt("Results.csv", Results, delimiter=",")

but there is a problem when I use after def normalize() in for-loop in spite of its error (ValueError) it also has warning FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead for D = dff.as_matrix().ravel() which is not important but right now since it is FutureWarning nevertheless I checked the shape of output was correct for 3 cycles by using print(data1.shape) and it was (3, 1440) which is 3 rows as 3 cycles and number of columns should be 3 times 480= 1440 but all in all wasn't stable solution.

the complete scripts are following:

import numpy as np
import pandas as pd
import os

def normalize(value, min_value, max_value, min_norm, max_norm):
    new_value = ((max_norm - min_norm)*((value - min_value)/(max_value - min_value))) + min_norm
    return new_value

#the size of matrices are (24,20)
df1 = np.zeros((24,20))
df2 = np.zeros((24,20))
df3 = np.zeros((24,20))


#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for cycle in range(3):
    count =  '{:04}'.format(cycle)
    j = cycle * 480
    new_value1 = df['A'].iloc[j:j+480]
    new_value2 = df['B'].iloc[j:j+480]
    new_value3 = df['C'].iloc[j:j+480]
    df1 = print_df(mkdf(new_value1))
    df2 = print_df(mkdf(new_value2))
    df3 = print_df(mkdf(new_value3))              
    for i in df:
        try:
            os.mkdir(i)
        except:
            pass
        min_val = df[i].min()
        min_nor = -1
        max_val = df[i].max()
        max_nor = 1
        ordered_data = mkdf(df.iloc[j:j+480][i])
        csv = print_df(ordered_data)
        #Print .csv files contains matrix of each parameters by name of cycles respectively
        csv.to_csv(f'{i}/{i}{count}.csv', header=None, index=None)            
        if 'C' in i:
            min_nor = -40
            max_nor = 150
            #Applying normalization for C between [-40,+150]
            new_value3 = normalize(df['C'].iloc[j:j+480], min_val, max_val, -40, 150)
            C_norm = print_df(mkdf(new_value3))
            C_norm.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)  
        else:
            #Applying normalization for A,B between    [-1,+1]
            new_value1 = normalize(df['A'].iloc[j:j+480], min_val, max_val, -1, 1)
            new_value2 = normalize(df['B'].iloc[j:j+480], min_val, max_val, -1, 1)
            A_norm = print_df(mkdf(new_value1))
            B_norm = print_df(mkdf(new_value2))
            A_norm.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None) 
            B_norm.to_csv(f'{i}/norm{i}{count}.csv', header=None, index=None)
    dff = pd.DataFrame({'A_norm':A_norm[cycle] , 'B_norm': B_norm[cycle] , 'C_norm': C_norm[cycle] } , index=[0])
    D = dff.as_matrix().ravel()
    if cycle == 0:
        Results = np.array(D)
    else:
        Results = np.vstack((Results, D))
    np.savetxt("Results.csv", Results , delimiter=',', encoding='utf-8')
#Check output shape whether is (3, 1440) or not 
data1 = np.loadtxt('Results.csv', delimiter=',')
print(data1.shape)

Note1: my data is txt file is following:

id_set: 000
     A: -2.46882615679
     B: -2.26408246559
     C: -325.004619528

Note2: I provided a dataset in text file for 3 cycles: Text dataset

Note3: for mapping A, B, C parameters into matrices in right order I used print_df() mkdf() functions but I didn't mention due to reduce it to the core problem and just leave a minimal example in start of this post. Let me know if you need that.

Expected result should be done by completing for-loop on 'A_norm','B_norm','C_norm' which are represented normalized versions of 'A','B','C' respectively and output let's call it "Results.csv" should be reversible to regenerate 'A','B','C' matrices through cycles again save them in csv. files for controlling , therefore if you have any ideas about reverse part please mention that separately otherwise just control it by using print(data.shape) and it should be (3, 1440). Have a nice day and thanks in advance!

来源：https://stackoverflow.com/questions/54537559/how-can-combine-3-matrices-into-1-matrice-with-reversible-approach

标签

python

numpy

dataframe

reshape

reversing