Using atmospy to load an SMPS File



In [19]:

    
import atmospy

print ("atmospy v{}".format(atmospy.__version__))









    



atmospy v0.1.0

dN Distribution (from SMPS)



In [20]:

    
# Setup an instance of the io.SMPS class
raw = atmospy.io.SMPS()

# Load the file of choice
%time raw.load("../tests/data/SMPS_Number.txt")









    



CPU times: user 91.8 ms, sys: 3.89 ms, total: 95.6 ms
Wall time: 94.2 ms

dN Distribution



In [21]:

    
%time d = atmospy.aerosols.ParticleDistribution(histogram = raw.histogram, bins = raw.bins)

%time d.compute()

%time d.statistics()









    



CPU times: user 9.93 ms, sys: 1.74 ms, total: 11.7 ms
Wall time: 10.2 ms
CPU times: user 9.55 ms, sys: 1.27 ms, total: 10.8 ms
Wall time: 10.2 ms
CPU times: user 458 ms, sys: 7.93 ms, total: 466 ms
Wall time: 461 ms



In [24]:

    
d.stats['Number'].head()









    Out[24]:






  
    
      
      N Total
      Mean
      GM
      GSD
    
    
      index
      
      
      
      
    
  
  
    
      2016-07-27 10:55:47
      148.973553
      119.463463
      100.673949
      1.913051
    
    
      2016-07-27 10:57:51
      156.962077
      113.179571
      94.575854
      1.946241
    
    
      2016-07-27 10:59:51
      153.911369
      102.963447
      77.151835
      2.464284
    
    
      2016-07-27 11:01:51
      144.782278
      112.479011
      93.247512
      1.977647
    
    
      2016-07-27 11:10:07
      130.507933
      122.642984
      102.040449
      2.043396



In [25]:

    
d.data['dN'].head()









    Out[25]:






  
    
      
      Bin 0
      Bin 1
      Bin 2
      Bin 3
      Bin 4
      Bin 5
      Bin 6
      Bin 7
      Bin 8
      Bin 9
      ...
      Bin 91
      Bin 92
      Bin 93
      Bin 94
      Bin 95
      Bin 96
      Bin 97
      Bin 98
      Bin 99
      Bin 100
    
    
      index
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      2016-07-27 10:55:47
      0.0000
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      3.31174
      2.53733
      7.47391
      1.28292
      0.934639
      4.88329
      3.690040
      0.970824
      3.162940
      2.23991
    
    
      2016-07-27 10:57:51
      0.0000
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      4.45274
      6.01509
      3.57956
      2.40033
      2.289790
      3.08258
      1.556350
      3.143430
      0.174966
      2.35769
    
    
      2016-07-27 10:59:51
      7.2512
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      4.30251
      1.54204
      3.26911
      1.20016
      5.342840
      1.54129
      2.334520
      0.785857
      0.000000
      2.41756
    
    
      2016-07-27 11:01:51
      0.0000
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      1.72908
      1.24506
      6.33078
      2.71221
      0.763263
      3.91952
      1.489400
      1.941650
      1.213770
      2.41756
    
    
      2016-07-27 11:10:07
      0.0000
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      0.0
      ...
      3.56038
      5.32061
      4.72268
      1.95619
      4.519640
      2.37246
      0.926519
      2.207760
      3.968480
      1.04141
    
  

5 rows × 101 columns



In [27]:

    
d.meta.head()



In [ ]:

	Dp	bin_left	bin_right	logDp	dDp	dlogDp
0	0.00661	0.006494	0.006732	-2.179799	0.000238	0.015625
1	0.00685	0.006732	0.006978	-2.164309	0.000247	0.015625
2	0.00710	0.006978	0.007234	-2.148742	0.000256	0.015623
3	0.00737	0.007234	0.007499	-2.132533	0.000265	0.015625
4	0.00764	0.007499	0.007774	-2.116907	0.000275	0.015625

	N Total	Mean	GM	GSD
index
2016-07-27 10:55:47	148.973553	119.463463	100.673949	1.913051
2016-07-27 10:57:51	156.962077	113.179571	94.575854	1.946241
2016-07-27 10:59:51	153.911369	102.963447	77.151835	2.464284
2016-07-27 11:01:51	144.782278	112.479011	93.247512	1.977647
2016-07-27 11:10:07	130.507933	122.642984	102.040449	2.043396

	Bin 0	Bin 1	Bin 2	Bin 3	Bin 4	Bin 5	Bin 6	Bin 7	Bin 8	Bin 9	...	Bin 91	Bin 92	Bin 93	Bin 94	Bin 95	Bin 96	Bin 97	Bin 98	Bin 99	Bin 100
index
2016-07-27 10:55:47	0.0000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	3.31174	2.53733	7.47391	1.28292	0.934639	4.88329	3.690040	0.970824	3.162940	2.23991
2016-07-27 10:57:51	0.0000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	4.45274	6.01509	3.57956	2.40033	2.289790	3.08258	1.556350	3.143430	0.174966	2.35769
2016-07-27 10:59:51	7.2512	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	4.30251	1.54204	3.26911	1.20016	5.342840	1.54129	2.334520	0.785857	0.000000	2.41756
2016-07-27 11:01:51	0.0000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	1.72908	1.24506	6.33078	2.71221	0.763263	3.91952	1.489400	1.941650	1.213770	2.41756
2016-07-27 11:10:07	0.0000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	3.56038	5.32061	4.72268	1.95619	4.519640	2.37246	0.926519	2.207760	3.968480	1.04141