In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from __future__ import division
from datetime import datetime,timedelta
%matplotlib inline
pd.options.display.max_rows = 99
pd.options.display.max_columns = 999
In [2]:
def pad(x):
time = x.astype(str).zfill(4)
return time[:2] + ':' + time[2:]
In [5]:
#contains headers for all data (I edited "nothing" column in header file b/c it's just 0's)
filefolder = 'data/sensor_data/colorado6months/'
desired_file = '20140401.csv'
def return_sensor_data(desired_file, filefolder):
'''Input: desired file and folder
Output: sensor data pandas dataframe
Usage: filefolder = 'data/sensor_data/colorado6months/'
desired_file = '20140401.csv'
return_sensor_data(myfile, filefolder)'''
df_header = pd.read_csv(filefolder + 'header.csv')
headers = df_header.columns
df_sensor = pd.read_csv(filefolder + desired_file,header=None) #sensors
df_sensor.columns = headers
df_sensor['MST'] = df_sensor['MST'].map(pad)
#make a datetime index:
df_sensor['datetime'] = pd.to_datetime(df_sensor['Year'].astype(str)+
df_sensor['DOY'].astype(str)+
df_sensor['MST'].astype(str),
format='%Y%j%H:%M')
#convert to UTC time, website shows that they disregard daylight savings time
df_sensor['datetime'] = df_sensor['datetime'] + pd.Timedelta(hours=7)
df_sensor.set_index(['datetime'],inplace=True) #set created column as index ...
df_sensor = df_sensor.resample('H')# ... so that we can resample it (hourly)
return df_sensor
return_sensor_data(desired_file, filefolder)
Out[5]:
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
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2014-04-01 07:00:00
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2.963834
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270.443300
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0
810.101383
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0
0
0.039349
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3.464543
2014-04-01 12:00:00
0
2014
91
0
93.591755
2.596963
2.510263
0.416570
8.155280
2.406760
2.734236
3.013713
3.375866
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0.000633
0.001550
0.000617
0.210350
0.001233
0.477633
0.003833
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0.003617
1.132023
2.686147
0.256138
1.089744
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5.294215
0.007383
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0.000011
1.148195
3.826909
20.860333
46.592833
5.920285
4.257295
2.661253
2.645205
6.777503
0.000056
238.659650
237.971633
306.476100
-84.955217
-71.691920
-5.699365
7.084622
4.462973
8.732907
0.174317
0.197617
1.421817
290.884850
1.829167
-1.179544
-0.000109
274.137583
274.469483
272.275250
272.140067
272.221683
22.023167
-1.038500
64.533333
24.697333
39.856333
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40.710333
39.698000
24.702500
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46.478333
1.000000
13.06
2.682483
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0.004150
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0.019338
5.550526
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305.310583
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270.539067
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0
0
809.461750
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0
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6.419112
2014-04-01 13:00:00
0
2014
91
0
82.140220
93.006725
102.601872
40.586209
112.272325
97.271930
100.844092
111.313385
106.246223
-0.000479
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4.712717
0.026550
4.531800
0.025050
0.014133
0.106783
347.214292
352.459132
342.261183
167.151123
340.639881
59.881038
2.379283
0.019017
0.732153
92.462173
43.406502
21.029333
45.595833
40.411885
101.230657
45.821745
45.404402
99.236167
0.000049
229.597533
228.486367
318.782333
-99.599148
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3.872481
101.643190
104.169135
46.372518
3.640083
4.494100
0.148050
11.116600
0.378333
344.744966
-0.000092
275.705000
275.972517
273.321567
273.132683
273.084200
22.005667
0.484117
59.935667
24.777500
39.887500
39.884333
40.735833
39.725333
24.792333
24.404500
46.508333
1.000000
13.06
2.686433
567.697802
2.011980
0.754383
0.599119
1.107887
0.283573
1.034495
0.460871
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0.262553
0.434281
188.089645
9.071122
28.161402
315.984333
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94.143645
226.328567
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0
0
809.186183
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0
0
0
0.044829
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0
194.050000
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47.932593
2014-04-01 14:00:00
0
2014
91
0
70.739575
309.078667
328.021617
133.951197
343.093483
320.398183
321.973167
335.894933
331.976933
-0.000448
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14.897333
0.107567
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801.450017
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757.532867
332.148600
796.333633
125.476875
17.487667
0.161317
2.280753
345.793000
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20.905500
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49.154942
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57.423580
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0.000042
224.955217
224.262667
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22.595795
333.606400
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1.971667
801.588200
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21.981000
2.546667
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24.805167
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1.000000
13.06
2.689567
1515.284833
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3.557645
1.060836
3.464137
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0.327283
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633.709817
30.701753
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0
0
808.839867
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0
0
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0
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58.362060
2014-04-01 15:00:00
0
2014
91
0
59.675297
515.724733
530.869983
217.584717
556.887833
526.208600
529.064717
537.373433
536.185000
-0.000428
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26.582333
0.307633
0.741867
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25.594500
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1.719000
850.945667
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27.100500
0.304583
2.560210
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78.924567
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528.009047
0.000011
234.388450
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47.275047
533.686533
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12.151500
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1.266583
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2.170000
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-0.000107
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21.998000
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46.608500
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13.06
2.689850
1650.863517
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4.454995
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0.568784
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1051.282233
50.274713
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387.098650
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125.097783
281.352617
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0.031833
0
0
808.563117
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0
0
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0
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0.1
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93.710953
2014-04-01 16:00:00
0
2014
91
0
49.526853
641.773733
650.031067
268.250167
678.837533
647.421233
647.844533
656.507150
654.817000
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34.627167
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1.317567
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3.052850
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473.442217
29.591050
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2.480198
657.486000
126.175005
20.850833
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107.915268
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645.495980
0.000034
246.152250
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421.995583
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61.727173
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1.523517
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283.911750
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1.000000
13.06
2.690000
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0
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0
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127.898515
2014-04-01 17:00:00
0
2014
91
0
41.196412
600.609083
587.337800
237.178222
616.113483
587.383550
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602.039367
592.597183
-0.000371
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3.390300
338.970440
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486.272650
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588.442567
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580.180125
0.000031
292.824450
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418.786800
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13.06
2.691000
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1190.709833
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0
0
807.772817
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0
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0
0
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0.1
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323.167590
2014-04-01 18:00:00
0
2014
91
0
36.092960
392.608133
390.294017
148.952277
409.233633
393.452417
396.336633
401.221767
393.053633
-0.000319
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1.168117
0.431917
23.060667
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23.142000
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298.899817
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797.031067
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0
807.336667
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0
0
0
0.069535
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193.990000
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0
0
4.633633
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0
5.729667
5.711267
0.1
5.338806
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229.143550
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0.164533
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335.654353
2014-04-01 19:00:00
0
2014
91
0
35.654707
475.058217
465.267183
184.811535
489.113217
465.713900
474.310733
478.947500
471.199283
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2.980200
148.562719
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55.988718
143.113575
321.919700
6.170583
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466.735983
339.347400
20.918333
43.230833
295.433583
462.311345
343.026717
341.179450
465.388892
0.000015
303.583233
303.343100
409.775033
-66.103842
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47.394700
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26.759500
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2.850000
143.800013
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285.493300
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283.131317
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22.141167
8.212800
41.031833
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39.946000
40.796333
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25.053333
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1.000000
13.06
2.690333
304.726351
6.823036
4.041615
3.148414
5.053619
1.519561
5.062003
2.754959
2.355458
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3.315795
950.773333
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0
0
806.843033
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0
0
0
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0
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0.1
4.561166
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341.726487
2014-04-01 20:00:00
0
2014
91
0
40.033195
601.494883
595.654600
243.419750
618.469217
597.310717
598.020183
600.819283
600.274200
-0.000262
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33.087333
0.632500
1.525567
0.583383
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3.534767
193.057962
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528.525317
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619.525750
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291.985117
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57.918085
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1.000000
13.06
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0
0
806.142533
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0
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0
0
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431.481208
2014-04-01 21:00:00
0
2014
91
0
47.914428
363.507883
363.376033
142.694905
377.115700
366.641850
368.235667
372.167783
366.009117
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1.890267
35.017409
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1.448700
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375.314550
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369.484118
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24.250977
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-0.000118
289.284883
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1.000000
13.06
2.690683
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729.937917
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0
0
805.930967
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0
0
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0.1
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337.405500
2014-04-01 22:00:00
0
2014
91
0
57.818428
242.212167
242.999267
94.553798
252.993033
245.911200
247.030617
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-0.000238
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250.733800
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1.000000
13.06
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0
0
805.995050
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0.1
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250.574370
2014-04-01 23:00:00
0
2014
91
0
68.759173
194.051233
198.755233
80.235270
206.265633
200.441350
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201.774083
201.052833
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209.350983
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0
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0.1
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195.728895
2014-04-02 00:00:00
0
2014
91
0
80.126183
85.737807
89.636593
36.325315
91.194758
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97.269875
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0.1
0.953873
-11.219233
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0.138023
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0.220093
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1007.645333
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11.681733
11.713883
81.466667
30.516667
234.166417
1368.468500
80.130844
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6.290051
67.084
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91.098543
2014-04-02 01:00:00
0
2014
91
0
91.522765
6.741764
6.805255
1.219648
5.463964
6.604028
6.819206
7.291261
6.583113
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8.707721
11.389517
20.882000
40.205333
10.147155
8.374545
6.855163
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11.439522
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277.714883
278.103617
353.235050
-105.442278
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11.524065
8.127080
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285.828717
286.251267
283.658667
284.073567
284.228000
22.217167
10.613000
25.913667
24.839000
39.943500
39.939000
40.786333
39.806167
25.049500
24.657167
46.611000
1.000000
13.06
2.683350
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0.066246
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0.045321
0.019810
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0.059741
12.675858
0.535282
2.671892
352.930683
-8.742796
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1.434193
1.591101
281.998217
3.823558
275.244033
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0
0
806.805967
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0
0
0
0.046647
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13.648000
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10.223833
27.864167
1.920650
91.554917
2.600000
0
194.001667
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0
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0
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0
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0.1
0.199782
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1008.081000
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11.139272
2014-04-02 02:00:00
0
2014
91
0
102.885935
-0.015336
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278.717733
279.310983
345.583833
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3.725237
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284.297467
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282.374850
282.516933
22.201333
9.273033
31.128500
24.835833
39.941500
39.930667
40.780000
39.793500
25.011000
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13.06
2.678533
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1.943042
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0
0
807.582933
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0
193.938333
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32.271667
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0.000000
0
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0
0
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0
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1008.858500
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102.914436
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3.387090
2014-04-02 03:00:00
0
2014
91
0
113.456267
-0.016716
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0.373369
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44.348333
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3.191833
172.661150
4.283333
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281.604600
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279.443983
279.891883
280.030567
22.198500
6.200317
50.359833
24.783000
39.893500
39.883000
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39.762167
24.915167
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46.564500
1.000000
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1.642840
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193.966667
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4.919000
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113.473283
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3.984577
2014-04-02 04:00:00
0
2014
91
0
122.816417
-0.019067
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2.340684
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3.711211
0.006733
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1.971323
20.875667
46.322500
4.617081
1.013030
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5.059500
0.000003
254.577117
254.522933
334.682200
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5.388363
1.099886
6.534989
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3.353717
329.161667
4.337500
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280.689533
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278.881583
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279.000150
22.207833
5.825450
49.572500
24.766667
39.883833
39.878667
40.744667
39.751333
24.897667
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1.000000
13.06
2.680600
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2.546550
334.129617
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277.413083
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251.725367
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0
808.103733
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0
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262.294333
2.766667
0
193.966667
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52.266833
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0.000000
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5.647667
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3.913067
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4.617081
2014-04-02 05:00:00
0
2014
91
0
130.164567
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243.356283
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5.066024
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279.905433
280.358367
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278.181117
22.182167
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0.000000
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4.288700
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2014-04-02 06:00:00
0
2014
91
0
134.449400
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4.487795
In [6]:
filefolder = 'data/pvoutput/pvoutput6months/'
desired_file = '20140401.csv'
def return_pvoutput_data(desired_file, filefolder):
'''Input: Desired file and folder
Output: PV Output dataframe
Usage: filefolder = 'data/pvoutput/pvoutput6months/'
desired_file = '20140401.csv'
return_pvoutput_data(desired_file, filefolder)'''
df_output = pd.read_csv(filefolder + desired_file) #pvoutput
#df_output['datetime'] = df_output['datetime'] + pd.Timedelta(hours=6) #convert to utc time
df_output['datetime'] = df_output['datetime'].apply(pd.to_datetime)
df_output['datetime'] = df_output['datetime'] + pd.Timedelta(hours=6) #convert to utc time
df_output.set_index(['datetime'],inplace=True) #set created column as index ...
df_output = df_output.resample('H') # ...so that we can resample it (hourly)
return df_output[['Power']]
return_pvoutput_data(desired_file, filefolder)
Out[6]:
Power
datetime
2014-04-01 06:00:00
0.000000
2014-04-01 07:00:00
0.000000
2014-04-01 08:00:00
0.000000
2014-04-01 09:00:00
0.000000
2014-04-01 10:00:00
0.000000
2014-04-01 11:00:00
0.000000
2014-04-01 12:00:00
8.333333
2014-04-01 13:00:00
669.416667
2014-04-01 14:00:00
3238.666667
2014-04-01 15:00:00
6951.916667
2014-04-01 16:00:00
9926.916667
2014-04-01 17:00:00
3256.666667
2014-04-01 18:00:00
5681.250000
2014-04-01 19:00:00
10425.083333
2014-04-01 20:00:00
5369.083333
2014-04-01 21:00:00
6518.333333
2014-04-01 22:00:00
3878.416667
2014-04-01 23:00:00
2280.083333
2014-04-02 00:00:00
1003.083333
2014-04-02 01:00:00
71.250000
2014-04-02 02:00:00
0.000000
2014-04-02 03:00:00
0.000000
2014-04-02 04:00:00
0.000000
2014-04-02 05:00:00
0.000000
In [7]:
%run data_helper_functions.py
from data_helper_functions import plot_satellite_image, return_satellite_data
In [8]:
filename = 'data/satellite/colorado/summer6months/data/goes15.2014.091.110018.BAND_06.nc'
plot_satellite_image(filename)
In [9]:
lons, lats, data = return_satellite_data(filename)
plt.figure(figsize=(8, 8))
imgplot = plt.imshow(data)
imgplot.set_interpolation('none')
In [ ]:
In [10]:
import matplotlib.pyplot as plt
%matplotlib inline
from datetime import datetime
from data_helper_functions import plot_satellite_image, \
return_satellite_data, find_closest_date, find_file_details, \
find_closest_date, find_desired_file
In [11]:
filefolder = "data/satellite/colorado/summer6months/data/"
desired_datetime = datetime(2014, 5, 5, 22) #year, month, day [, hour, minute, second]
desired_channel = 'BAND_01' #channel
sat_image_file = filefolder+find_desired_file(desired_datetime, desired_channel, filefolder)
lons, lats, data = return_satellite_data(sat_image_file)
plt.figure(figsize=(8, 8))
imgplot = plt.imshow(data)
imgplot.set_interpolation('none')
plt.show()
In [12]:
import os
from datetime import datetime
def find_file_from_date(desired_datetime, filefolder, filetype = 'csv'):
'''Input: datetime, folder, filetype; Output: file'''
data_files = os.listdir(filefolder)
list_of_files = [] #contains all the files
list_of_files_details = [] #contains information collected from filename
for myfile in data_files:
if (myfile[-2:] == filetype or myfile[-3:] == filetype): #only get netCDF by default
list_of_files.append(myfile)
list_of_files_details.append(myfile.split('.'))
for i,val in enumerate(list_of_files_details):
try:
if datetime.strptime(list_of_files_details[i][0], '%Y%m%d') == desired_datetime:
return list_of_files[i]
except:
pass
filefolder = "data/pvoutput/pvoutput6months/"
desired_datetime = datetime(2014, 5, 5) #year, month, day [, hour, minute, second]
find_file_from_date(desired_datetime, filefolder)
Out[12]:
'20140505.csv'
In [ ]:
Content source: scottlittle/solar-sensors
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