In [8]:

    

import sys, os

import pandas as pd
import numpy as np

from glob import glob
from tqdm import tqdm


    

In [9]:

    

house3_c1 = np.loadtxt( 'datasets/REDD/high_freq/house_3/current_1.dat' )


    

In [7]:

    

house3_c1.shape


    

    
Out[7]:





(71202, 277)

In [6]:

    

house3_c1[:,1]


    

    
Out[6]:





array([ 1782.,  1782.,  2760., ...,  6802., 10603.,  3760.])

In [7]:

    

df_house3_c1 = pd.DataFrame(
    data = house3_c1, 
    columns=['utc_timestamp', 'cycle_count'] + ['wf_value_'+str(i) for i in range(0, 275)]
)
df_house3_c1.head()


    

    
Out[7]:







  

    

      

      
utc_timestamp
      
cycle_count
      
wf_value_0
      
wf_value_1
      
wf_value_2
      
wf_value_3
      
wf_value_4
      
wf_value_5
      
wf_value_6
      
wf_value_7
      
...
      
wf_value_265
      
wf_value_266
      
wf_value_267
      
wf_value_268
      
wf_value_269
      
wf_value_270
      
wf_value_271
      
wf_value_272
      
wf_value_273
      
wf_value_274
    
  
  

    

      
0
      
1.302922e+09
      
1782.0
      
-0.017231
      
-0.017167
      
-0.017059
      
-0.017008
      
-0.017104
      
-0.017163
      
-0.017450
      
-0.017587
      
...
      
-0.017431
      
-0.017421
      
-0.017433
      
-0.017398
      
-0.017257
      
-0.017344
      
-0.017262
      
-0.017277
      
-0.017192
      
-0.017232
    
    

      
1
      
1.302922e+09
      
1782.0
      
-0.017288
      
-0.017300
      
-0.017320
      
-0.017308
      
-0.017248
      
-0.017345
      
-0.017636
      
-0.017700
      
...
      
-0.017539
      
-0.017615
      
-0.017604
      
-0.017637
      
-0.017505
      
-0.017600
      
-0.017512
      
-0.017387
      
-0.017450
      
-0.017289
    
    

      
2
      
1.302931e+09
      
2760.0
      
0.002427
      
0.007893
      
0.020264
      
0.037044
      
0.059954
      
0.139391
      
0.195261
      
0.231653
      
...
      
-0.131324
      
-0.208694
      
-0.192772
      
-0.104762
      
-0.070186
      
-0.036816
      
-0.027137
      
-0.012471
      
-0.003119
      
0.002425
    
    

      
3
      
1.302931e+09
      
2065.0
      
0.004701
      
0.008471
      
0.022125
      
0.040235
      
0.063376
      
0.143110
      
0.198713
      
0.237631
      
...
      
-0.131911
      
-0.212030
      
-0.197415
      
-0.107624
      
-0.073997
      
-0.036811
      
-0.027118
      
-0.013915
      
-0.003341
      
0.004708
    
    

      
4
      
1.302931e+09
      
206.0
      
0.002921
      
0.006746
      
0.021267
      
0.042351
      
0.065329
      
0.144688
      
0.197718
      
0.238668
      
...
      
-0.216036
      
-0.261511
      
-0.158430
      
-0.126622
      
-0.074224
      
-0.036602
      
-0.027803
      
-0.014041
      
-0.005268
      
0.002964
    
  

5 rows × 277 columns

In [8]:

    

import matplotlib.pyplot as plt

%matplotlib inline


    

In [9]:

    

plt.plot(house3_c1[0,2:])


    

    
Out[9]:





[<matplotlib.lines.Line2D at 0x7f09320a6400>]






    

In [10]:

    

plt.plot(house3_c1[1,2:])


    

    
Out[10]:





[<matplotlib.lines.Line2D at 0x7f098ac25a20>]






    

In [12]:

    

!!pip install tqdm


    

    
Out[12]:





['Collecting tqdm',
 '  Using cached https://files.pythonhosted.org/packages/9f/3d/7a6b68b631d2ab54975f3a4863f3c4e9b26445353264ef01f465dc9b0208/tqdm-4.32.2-py2.py3-none-any.whl',
 'Installing collected packages: tqdm',
 'Successfully installed tqdm-4.32.2']

In [ ]:

    

xlabels = list(range(0, 275, 5))

for r in tqdm(range(0, house3_c1.shape[0])):

    name_fig = 'datasets/REDD/high_freq/{}/{}/wf_{}.png'.format( 'house_3', 'current_1', house3_c1[r,0] )
    
    if not os.path.isfile(name_fig):
        fig = plt.figure(figsize=(15,5))
        ax = fig.add_axes([0.1,0.1,0.8,0.8])

        ax.plot(house3_c1[r,2:])
        ax.grid()
        ax.set_xticks(xlabels )
        ax.set_xticklabels(xlabels, rotation=90)
        ax.set_xlim(0, 274)
        ax.set_title('Timestamp: {}'.format(house3_c1[r,0]))

        fig.savefig(name_fig)   # save the figure to file
        plt.close(fig)    # close the figure

#plt.plot(house3_c1[8,2:])


    

    




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