notebook.community

Edit and run

Summon any data

I want to make a single query and have it return data across the datasets





In [18]:



    


from datetime import datetime,timedelta, time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from data_helper_functions import *
from IPython.display import display
pd.options.display.max_columns = 999
%matplotlib inline



desired_channel = 'BAND_01'
desired_date = datetime(2014, 4, 1)
desired_timedelta = timedelta(hours = 15)
desired_datetime = desired_date + desired_timedelta
satellite_filefolder = '../../data/satellite/colorado/summer6months/data/'
sensor_filefolder = '../../data/sensor_data/colorado6months/'
pvoutput_filefolder = '../../data/pvoutput/pvoutput6months/'

#satellite data
satellite_filename = find_filename(desired_datetime, desired_channel, satellite_filefolder)
lons, lats, data = return_satellite_data(satellite_filename, satellite_filefolder)


plt.figure(figsize=(8, 8))
imgplot = plt.imshow(data)
imgplot.set_interpolation('none')
plt.savefig('foo.png')
plt.show()

#sensor data
sensor_filename = find_file_from_date(desired_date, sensor_filefolder)
df_sensor = return_sensor_data(sensor_filename, sensor_filefolder)
df_sensor[df_sensor.index == desired_datetime]
display(df_sensor[df_sensor.index == desired_datetime])

#pvoutput data
pvoutput_filename = find_file_from_date(desired_date, pvoutput_filefolder)
df_pvoutput = return_pvoutput_data(pvoutput_filename, pvoutput_filefolder)
display(df_pvoutput[df_pvoutput.index == desired_datetime])

#saving df to image

# a = Image(data=df_sensor)
# type(a)



    


















    






('.py', 'U', 1)










    

















    










  
    

      
      nothing
      Year
      DOY
      CR3000 CF Change [counts]
      CR3000 Zen Angle [degrees]
      Global LI-200 [W/m^2]
      Global CM22 (vent/cor) [W/m^2]
      Global RG780 PSP (vent/cor) [W/m^2]
      Global TSP-1 [W/m^2]
      Global CM6b (cor) [W/m^2]
      Global SP Lite [W/m^2]
      Global SP-110 [W/m^2]
      Global TSP-700 Vent [W/m^2]
      Research 1
      Research 2
      Global TUVR [W/m^2]
      Global 501A [W/m^2]
      Global 501A [MED/hr]
      Global MS210W [W/m^2]
      Global CUVA1 [W/m^2]
      Global CUVB1 [W/m^2]
      Global UV-S-A-T [W/m^2]
      Global UV-S-B-T [W/m^2]
      Global UVB-1 [W/m^2]
      Global 501A [Index]
      Direct NIP #1 [W/m^2]
      Direct NIP #2 [W/m^2]
      Direct LI-201 [W/m^2]
      Direct RG780 NIP [W/m^2]
      Direct CH1 [W/m^2]
      Zebra PSP (cor) [W/m^2]
      Direct CUVA2 [W/m^2]
      Direct CUVB2 [W/m^2]
      500nm TWC Photometer [V]
      Global SPN1 [W/m^2]
      Diffuse SPN1 [W/m^2]
      Data lab Dry Bulb Temp [deg C]
      Data lab RH [%]
      Diffuse PSP (sband/cor) [W/m^2]
      Research F1
      Diffuse 8-48 (vent) [W/m^2]
      Diffuse CM22 (vent/cor) [W/m^2]
      Research F0
      Research 3
      Downwelling IR PIR Vent [W/m^2]
      Downwelling IR CG4 Vent [W/m^2]
      Upwelling IR PIR [W/m^2]
      Instrument Net DW PIR [W/m^2]
      Instrument Net DW CG4 [W/m^2]
      Instrument Net UW PIR [W/m^2]
      Global PSP (cor) [W/m^2]
      Global PSP (vent/cor) [W/m^2]
      Diffuse PSP (vent/cor) [W/m^2]
      Diffuse CUV4 [W/m^2]
      Global CUV4 [W/m^2]
      Avg Wind Speed @ 19ft [m/s]
      Avg Wind Direction @ 19ft [deg from N]
      Peak Wind Speed @ 19ft [m/s]
      Direct MS-56 [W/m^2]
      Research 4
      PIR DW Dome Temp [deg K]
      PIR DW Case Temp [deg K]
      CG4 DW Case Temp [deg K]
      PIR UW Dome Temp [deg K]
      PIR UW Case Temp [deg K]
      CR3000 Temp [deg C]
      Deck Dry Bulb Temp [deg C]
      Deck RH [%]
      501A Temp [deg C]
      CUVA1 Temp [deg C]
      CUVB1 Temp [deg C]
      CUVA2 Temp [deg C]
      CUVB2 Temp [deg C]
      UVSAT Temp [deg C]
      UVSBT Temp [deg C]
      UVB-1 Temp [deg C]
      Horiz TP Thermal Corr
      CR3000 Battery [VDC]
      CR3000 Pgm Time [s]
      Direct Quantum LI-190 [umol/s/m^2]
      Direct TUVR [W/m^2]
      Global PSP [mV]
      Global PSP Vent [mV]
      Global CM22 [mV]
      Global RG780 PSP [mV]
      Global CM6b [mV]
      Zebra PSP [mV]
      Diffuse PSP (sband) [mV]
      Diffuse PSP [mV]
      Diffuse CM22 [mV]
      Global Quantum LI-190 [umol/s/m^2]
      Global Photometric LI-210 [klux]
      Upwelling Shortwave CM3 (CNR1) [W/m^2]
      Upwelling IR CG3 (CNR1) [W/m^2]
      Instrument Net UW CG3 [W/m^2]
      Upwelling Shortwave PSP [W/m^2]
      Upwelling Shortwave LI-200 [W/m^2]
      Upwelling Quantum LI-190 [umol/s/m^2]
      CNR1 Case Temp [deg K]
      Global CM3 (CNR1) [W/m^2]
      Downwelling IR CG3 (CNR1) [W/m^2]
      Instrument Net DW CG3 [W/m^2]
      Snow Depth [cm]
      Precipitation [mm]
      Precipitation (Accumulated) [mm]
      Station Pressure [mBar]
      Global 40-South PSP [W/m^2]
      Global 40-South LI-200 [W/m^2]
      Global Normal CM-21 [W/m^2]
      Global 90-North PSP [W/m^2]
      Global 90-North LI-200 [W/m^2]
      Global 90-East PSP [W/m^2]
      Global 90-East LI-200 [W/m^2]
      Global 90-South PSP [W/m^2]
      Global 90-South LI-200 [W/m^2]
      Global 90-West PSP [W/m^2]
      Global 90-West LI-200 [W/m^2]
      Research RT0
      Research RT1
      Research RT2
      Atmospheric Electric Field [kV/m]
      CR10X Temp (Rad-Twr) [deg C]
      CR10X Battery (Rad-Twr) [VDC]
      LI-2020 Battery [VDC]
      Tower Dry Bulb Temp [deg C]
      Tower RH [%]
      Avg Wind Speed @ 6ft [m/s]
      Avg Wind Direction @ 6ft [deg from N]
      Peak Wind Speed @ 6ft [m/s]
      CR10X Overuns (Rad-Twr) [counts]
      Snow Depth Quality
      SE Dry Bulb Temp [deg C]
      SE RH [%]
      SE-POA Angle [degrees]
      Global SE-POA LI-200 [W/m^2]
      CR10X Overuns (Met-Twr) [counts]
      CR10X Temp (Met-Twr) [deg C]
      CR10X Battery (Met-Twr) [VDC]
      Vertical Wind Shear [1/s]
      Research PVT1
      Research PVT2
      Avg Wind Speed @ 22ft [m/s]
      Avg Wind Direction @ 22ft [deg from N]
      Avg Wind Speed @ 42ft [m/s]
      Avg Wind Direction @ 42ft [deg from N]
      Research PVT0
      Peak Wind Speed @ 22ft [m/s]
      Peak Wind Speed @ 42ft [m/s]
      Delta UT1 [seconds]
      500nm TWC AOD
      Net Radiation Eppley [W/m^2]
      Net Radiation K&Z [W/m^2]
      Atmos Net Infrared PIRs [W/m^2]
      Atmos Net Infrared K&Zs [W/m^2]
      Albedo (PSP)
      Albedo (K&Z)
      Albedo (LI-200)
      Albedo Quantum (LI-190)
      Broadband Turbidity
      500nm Estimated AOD
      Sea-Level Pressure (Est) [mBar]
      Tower Dew Point Temp [deg C]
      Tower Wet Bulb Temp [deg C]
      Tower Wind Chill Temp [deg C]
      Deck Wind Chill Temp [deg C]
      Total Cloud Cover [%]
      Opaque Cloud Cover [%]
      Global Extraterrestrial (calc) [W/m^2]
      Direct Extraterrestrial (calc) [W/m^2]
      Zenith Angle [degrees]
      Azimuth Angle [degrees]
      Airmass
      Delta T [seconds]
      315nm POM-01 Photometer [nA]
      400nm POM-01 Photometer [uA]
      500nm POM-01 Photometer [uA]
      675nm POM-01 Photometer [uA]
      870nm POM-01 Photometer [uA]
      940nm POM-01 Photometer [uA]
      1020nm POM-01 Photometer [uA]
      315nm Obsolete AOD
      400nm Obsolete AOD
      500nm Obsolete AOD
      675nm Obsolete AOD
      870nm Obsolete AOD
      940nm Obsolete AOD
      1020nm Obsolete AOD
      Research F2
    

    

      datetime
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      2014-04-01 15:00:00
      0
      2014
      91
      0
      59.675297
      515.724733
      530.869983
      217.584717
      556.887833
      526.2086
      529.064717
      537.373433
      536.185
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      26.582333
      0.307633
      0.741867
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      26.423833
      0.2669
      25.5945
      0.311583
      0.286367
      1.719
      850.945667
      854.891467
      785.683133
      335.8888
      847.10945
      316.697337
      27.1005
      0.304583
      2.56021
      545.086617
      86.807618
      20.894333
      45.554833
      78.924567
      526.211737
      97.484863
      94.525517
      528.009047
      0.000011
      234.38845
      233.4297
      391.260367
      -120.161483
      -96.585663
      47.275047
      533.686533
      530.449917
      81.929157
      12.1515
      26.266
      1.266583
      61.042983
      2.17
      848.06905
      -0.000107
      280.882033
      281.200233
      276.84915
      279.484283
      279.256333
      21.998
      3.580217
      51.199
      24.853
      39.923833
      39.919
      40.767167
      39.756667
      24.961167
      24.633
      46.6085
      0.996667
      13.06
      2.68985
      1650.863517
      27.757582
      4.454995
      3.48874
      5.763251
      1.76781
      5.709255
      2.6807
      0.568784
      0.503295
      0.911403
      1051.282233
      50.274713
      89.846665
      387.09865
      27.285707
      100.039898
      122.379018
      125.097783
      281.352617
      528.731533
      237.59495
      -116.501485
      0.031833
      0
      0
      808.563117
      596.762067
      637.909267
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      103.095687
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      815.597267
      870.38775
      362.298317
      392.404933
      109.173517
      117.1579
      0
      0
      0
      0.108705
      8.68675
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      3.940467
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      2.320833
      0
      194.063333
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      0
      4.178967
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      0
      1.642017
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      2.496667
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      0.1
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      -153.66895
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      0.238087
      0.119153
      0.122041
      0.169344
      1009.838333
      -4.053717
      0.351283
      2.80995
      2.694483
      8.533333
      1.833333
      690.108133
      1368.76055
      59.673836
      111.437533
      1.994829
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      -2307.7974
      -2215.35275
      -2157.977917
      -2198.435917
      -2259.567083
      -2229.510583
      -2332.943257
      -2332.8457
      -2332.908597
      -2332.933071
      -2332.944231
      -2332.805651
      -2332.936275
      93.710953
    

  













    










  
    

      
      Power
    

    

      datetime
      
    

  
  
    

      2014-04-01 15:00:00
      6951.916667

Build up sensor to pvoutput model





In [1]:



    


from datetime import datetime,timedelta, time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from data_helper_functions import *
from IPython.display import display
pd.options.display.max_columns = 999
%matplotlib inline

#iterate over datetimes:
mytime = datetime(2014, 4, 1, 13)
times = make_time(mytime)

# Now that we can call data up over any datetime and we have a list of interested datetimes,
# we can finally construct an X matrix and y vector for regression.

sensor_filefolder = 'data/sensor_data/colorado6months/'
pvoutput_filefolder = 'data/pvoutput/pvoutput6months/'

X = []
y = []

for desired_datetime in times:
    
    try: #something wrong with y on last day
        desired_date = (desired_datetime - timedelta(hours=6)).date() #make sure correct date
        desired_date = datetime.combine(desired_date, time.min) #get into datetime format

        sensor_filename = find_file_from_date(desired_date, sensor_filefolder)
        df_sensor = return_sensor_data(sensor_filename, sensor_filefolder).ix[:,-15:-1]
        df_sensor[df_sensor.index == desired_datetime]

        pvoutput_filename = find_file_from_date(desired_date, pvoutput_filefolder)
        df_pvoutput = return_pvoutput_data(pvoutput_filename, pvoutput_filefolder)
        
        y.append(df_pvoutput[df_pvoutput.index == desired_datetime].values[0][0])
        X.append(df_sensor[df_sensor.index == desired_datetime].values[0])
    except:
        pass



    











In [2]:



    


X = np.array(X)
y = np.array(y)



    











In [3]:



    


print X.shape
print y.shape



    


















    






(2729, 14)
(2729,)

...finally ready to model!

Random Forest





In [4]:



    


from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=99)



    











In [5]:



    


from sklearn.ensemble import RandomForestRegressor
rfr = RandomForestRegressor(oob_score = True)



    











In [6]:



    


rfr.fit(X_train,y_train)



    


















    






/home/ubuntu/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.py:659: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.
  warn("Some inputs do not have OOB scores. "










    
Out[6]:








RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
           min_samples_split=2, min_weight_fraction_leaf=0.0,
           n_estimators=10, n_jobs=1, oob_score=True, random_state=None,
           verbose=0, warm_start=False)

















In [7]:



    


y_pred = rfr.predict(X_test)



    











In [8]:



    


rfr.score(X_test,y_test)



    


















    
Out[8]:








0.7416004420473985

















In [9]:



    


df_sensor.columns.values.shape



    


















    
Out[9]:








(14,)

















In [10]:



    


sorted_mask = np.argsort(rfr.feature_importances_)

for i in zip(df_sensor.columns.values,rfr.feature_importances_[sorted_mask])[::-1]:
    print i



    


















    






('1020nm Obsolete AOD', 0.29750646320870311)
('940nm Obsolete AOD', 0.14914504337436166)
('870nm Obsolete AOD', 0.10411649689061561)
('675nm Obsolete AOD', 0.074624089606396909)
('500nm Obsolete AOD', 0.069367206965655653)
('400nm Obsolete AOD', 0.063151927931135138)
('315nm Obsolete AOD', 0.043132327530184388)
('1020nm POM-01 Photometer [uA]', 0.043044870168825564)
('940nm POM-01 Photometer [uA]', 0.033910189598308257)
('870nm POM-01 Photometer [uA]', 0.032363587980892075)
('675nm POM-01 Photometer [uA]', 0.03067425313525558)
('500nm POM-01 Photometer [uA]', 0.019825613007849133)
('400nm POM-01 Photometer [uA]', 0.019767936090060254)
('315nm POM-01 Photometer [nA]', 0.019369994511756652)

Linear model





In [12]:



    


#now do a linear model and compare:
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train,y_train)
lr.score(X_test,y_test)



    


















    
Out[12]:








-48.833843056592052

















In [13]:



    


sorted_mask = np.argsort(lr.coef_)

for i in zip(df_sensor.columns.values,lr.coef_[sorted_mask])[::-1]:
    print i



    


















    






('1020nm Obsolete AOD', 25720.514248532258)
('940nm Obsolete AOD', 5787.6495593808249)
('870nm Obsolete AOD', 924.66656776148329)
('675nm Obsolete AOD', 266.2149419573488)
('500nm Obsolete AOD', 254.70915555929054)
('400nm Obsolete AOD', 0.18497028763408108)
('315nm Obsolete AOD', -12.098032718226079)
('1020nm POM-01 Photometer [uA]', -78.41950447932544)
('940nm POM-01 Photometer [uA]', -170.39792476852338)
('870nm POM-01 Photometer [uA]', -260.19451251012526)
('675nm POM-01 Photometer [uA]', -2952.8050029904844)
('500nm POM-01 Photometer [uA]', -3374.2232859668857)
('400nm POM-01 Photometer [uA]', -6081.5228897332427)
('315nm POM-01 Photometer [nA]', -20024.56399336789)

















In [14]:



    


df_sensor.ix[:,-15:-1].head() #selects photometer and AOD, 
# useful in next iteration of using sensor data to fit



    


















    
Out[14]:










  
    

      
      315nm POM-01 Photometer [nA]
      400nm POM-01 Photometer [uA]
      500nm POM-01 Photometer [uA]
      675nm POM-01 Photometer [uA]
      870nm POM-01 Photometer [uA]
      940nm POM-01 Photometer [uA]
      1020nm POM-01 Photometer [uA]
      315nm Obsolete AOD
      400nm Obsolete AOD
      500nm Obsolete AOD
      675nm Obsolete AOD
      870nm Obsolete AOD
      940nm Obsolete AOD
    

    

      datetime
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      2014-09-29 07:00:00
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
    

    

      2014-09-29 08:00:00
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
    

    

      2014-09-29 09:00:00
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
    

    

      2014-09-29 10:00:00
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
    

    

      2014-09-29 11:00:00
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0
      0

When only keeping the photometer data, random forest and linear model do pretty similar. When I added all of the sensor instruments to the fit, rfr scored 0.87 and lr scored negative!

Also, I threw away the mysterious "Research 2" sensor, that was probably just a solar panel! I asked NREL what it is, so we'll see. If it turns out to be a solar panel, then I can do some feature engineering with the sensor data by simulating a solar panel!

Neural Net Exploration





In [313]:



    


import pandas as pd
import numpy as np
from sklearn.preprocessing import scale
from lasagne import layers
from lasagne.nonlinearities import  softmax, rectify, sigmoid, linear, very_leaky_rectify, tanh
from lasagne.updates import nesterov_momentum, adagrad, momentum
from nolearn.lasagne import NeuralNet
import theano
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler



    











In [314]:



    


y = y.astype('float32')
x = X.astype('float32')
scaler = StandardScaler()
scaled_x = scaler.fit_transform(x)
x_train, x_test, y_train, y_test = train_test_split(scaled_x, y, test_size = 0.2, random_state = 12)



    











In [371]:



    


nn_regression = NeuralNet(layers=[('input', layers.InputLayer),
#                                   ('hidden1', layers.DenseLayer),
#                                    ('hidden2', layers.DenseLayer),
                                  ('output', layers.DenseLayer)
                                ],

               # Input Layer
               input_shape=(None, x.shape[1]),

               # hidden Layer
#                hidden1_num_units=512,
#                hidden1_nonlinearity=softmax,
                          
             # hidden Layer
#                hidden2_num_units=128,
#                hidden2_nonlinearity=linear,

              # Output Layer
              output_num_units=1,
              output_nonlinearity=very_leaky_rectify,

              # Optimization
              update=nesterov_momentum,
              update_learning_rate=0.03,#0.02
              update_momentum=0.8,#0.8
              max_epochs=600, #was 100

              # Others
              #eval_size=0.2,
              regression=True,
              verbose=0,
         )



    











In [372]:



    


nn_regression.fit(x_train, y_train)
y_pred = nn_regression.predict(x_test)
nn_regression.score(x_test, y_test)



    


















    
Out[372]:








14222263.690682003

















In [373]:



    


val = 11
print y_pred[val][0]
print y_test[val]



    


















    






4135.87530116
2604.0

















In [374]:



    


plt.plot(y_pred,'ro')



    


















    
Out[374]:








[<matplotlib.lines.Line2D at 0x108d89250>]










    
























In [336]:



    


plt.plot(y_test,'go')



    


















    
Out[336]:








[<matplotlib.lines.Line2D at 0x10e5c7dd0>]

Extra Trees!





In [68]:



    


from sklearn.ensemble import ExtraTreesRegressor
etr = ExtraTreesRegressor(oob_score=True, bootstrap=True,
                          n_jobs=-1, n_estimators=1000) #nj_obs uses all cores!
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=99)



    











In [69]:



    


etr.fit(X_train, y_train)



    


















    
Out[69]:








ExtraTreesRegressor(bootstrap=True, criterion='mse', max_depth=None,
          max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,
          min_samples_split=2, min_weight_fraction_leaf=0.0,
          n_estimators=1000, n_jobs=-1, oob_score=True, random_state=None,
          verbose=0, warm_start=False)

















In [70]:



    


print etr.score(X_test,y_test)
print etr.oob_score_



    


















    






0.791134847605
0.764207504423

















In [87]:



    


y_pred = etr.predict(X_test)

from random import randint
val = randint(0,y_test.shape[0])
print y_pred[val]
print y_test[val]



    


















    






5588.96858333
6567.0

















In [71]:



    


print X.shape
print y.shape



    


















    






(2729, 14)
(2729,)

Save this thing and try it out on the simulated sensors!





In [91]:



    


from sklearn.externals import joblib
joblib.dump(etr, 'data/sensor-to-power-model/sensor-to-power-model.pkl')



    


















    
Out[91]:








['data/sensor-to-power-model/sensor-to-power-model.pkl',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_01.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_02.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_03.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_04.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_05.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_06.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_07.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_08.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_09.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_10.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_11.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_12.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_13.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_14.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_15.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_16.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_17.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_18.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_19.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_20.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_21.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_22.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_23.npy',
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 'data/sensor-to-power-model/sensor-to-power-model.pkl_38.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_39.npy',
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 'data/sensor-to-power-model/sensor-to-power-model.pkl_41.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_42.npy',
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 'data/sensor-to-power-model/sensor-to-power-model.pkl_45.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_46.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_47.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_48.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_49.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_50.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_51.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_52.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_53.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_54.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_55.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_56.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_57.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_58.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_59.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_60.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_61.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_62.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_63.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_64.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_65.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_66.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_67.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_68.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_69.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_70.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_71.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_72.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_73.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_74.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_75.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_76.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_77.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_78.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_79.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_80.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_81.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_82.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_83.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_84.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_85.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_86.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_87.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_88.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_89.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_90.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_91.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_92.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_93.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_94.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_95.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_96.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_97.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_98.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_99.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_100.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_101.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_102.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_103.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_104.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_105.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_106.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_107.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_108.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_109.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_110.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_111.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_112.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_113.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_114.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_115.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_116.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_117.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_118.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_119.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_120.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_121.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_122.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_123.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_124.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_125.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_126.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_127.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_128.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_129.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_130.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_131.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_132.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_133.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_134.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_135.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_136.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_137.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_138.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_139.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_140.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_141.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_142.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_143.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_144.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_145.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_146.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_147.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_148.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_149.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_150.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_151.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_152.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_153.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_154.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_155.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_156.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_157.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_158.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_159.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_160.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_161.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_162.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_163.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_164.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_165.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_166.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_167.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_168.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_169.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_170.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_171.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_172.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_173.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_174.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_175.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_176.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_177.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_178.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_179.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_180.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_181.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_182.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_183.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_184.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_185.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_186.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_187.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_188.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_189.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_190.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_191.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_192.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_193.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_194.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_195.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_196.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_197.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_198.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_199.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_200.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_201.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_202.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_203.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_204.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_205.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_206.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_207.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_208.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_209.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_210.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_211.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_212.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_213.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_214.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_215.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_216.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_217.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_218.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_219.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_220.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_221.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_222.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_223.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_224.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_225.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_226.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_227.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_228.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_229.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_230.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_231.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_232.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_233.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_234.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_235.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_236.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_237.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_238.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_239.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_240.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_241.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_242.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_243.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_244.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_245.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_246.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_247.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_248.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_249.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_250.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_251.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_252.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_253.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_254.npy',
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 'data/sensor-to-power-model/sensor-to-power-model.pkl_823.npy',
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 'data/sensor-to-power-model/sensor-to-power-model.pkl_825.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_826.npy',
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 'data/sensor-to-power-model/sensor-to-power-model.pkl_898.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_899.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_900.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_901.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_902.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_903.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_904.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_905.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_906.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_907.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_908.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_909.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_910.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_911.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_912.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_913.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_914.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_915.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_916.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_917.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_918.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_919.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_920.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_921.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_922.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_923.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_924.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_925.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_926.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_927.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_928.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_929.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_930.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_931.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_932.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_933.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_934.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_935.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_936.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_937.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_938.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_939.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_940.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_941.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_942.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_943.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_944.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_945.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_946.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_947.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_948.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_949.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_950.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_951.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_952.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_953.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_954.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_955.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_956.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_957.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_958.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_959.npy',
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 'data/sensor-to-power-model/sensor-to-power-model.pkl_962.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_963.npy',
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 'data/sensor-to-power-model/sensor-to-power-model.pkl_966.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_967.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_968.npy',
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 'data/sensor-to-power-model/sensor-to-power-model.pkl_970.npy',
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 'data/sensor-to-power-model/sensor-to-power-model.pkl_975.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_976.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_977.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_978.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_979.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_980.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_981.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_982.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_983.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_984.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_985.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_986.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_987.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_988.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_989.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_990.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_991.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_992.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_993.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_994.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_995.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_996.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_997.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_998.npy',
 'data/sensor-to-power-model/sensor-to-power-model.pkl_999.npy',
 ...]

















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np.savez_compressed('data/y.npz',y=y) #save y



    











In [ ]:

Content source: scottlittle/solar-sensors

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