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In [ ]:



In [4]:

    
import atmPy



In [7]:

    
fname = '/Volumes/HTelg_4TB_Backup/arm_data/SGP/aossmpsE13/sgpaossmpsE13.a1.20161118.000048.nc'
aossmps = atmPy.data_archives.arm.open_path(fname)









    



Opening 1 files.
{'suffix': '.nc', 'product': 'aossmps', 'facility': 'E13', 'site': 'sgp', 'timestamp': Timestamp('2016-11-18 00:00:48'), 'qc_level': 'a1'}



In [8]:

    
aossmps.plot()









    Out[8]:





(<Figure size 432x288 with 2 Axes>,
 <matplotlib.axes._subplots.AxesSubplot at 0x1d1fc29470>,
 <matplotlib.collections.QuadMesh at 0x1d1f3a9048>,
 <matplotlib.colorbar.Colorbar at 0x1d1eb2b518>)

xarray



In [5]:

    
fname = '/Volumes/HTelg_4TB_Backup/arm_data/SGP/aossmpsE13/sgpaossmpsE13.a1.20161117.000000.nc'
ds = xr.open_dataset(fname, )



In [15]:

    
(~np.isnan(ds.number_size_distribution.data.flatten())).sum()









    Out[15]:





27144



In [29]:

    
(~np.isnan(ds.number_size_distribution.data.flatten())).shape









    Out[29]:





(50112,)



In [17]:

    
import atmPy



In [36]:

    
i = 220
ds.number_size_distribution#.to_pandas().iloc[i:i+20,80:100]









    Out[36]:





<xarray.DataArray 'number_size_distribution' (time: 261, diameter_midpoint: 192)>
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)
Coordinates:
  * time               (time) datetime64[ns] 2016-11-17 ... 2016-11-17T23:55:48
  * diameter_midpoint  (diameter_midpoint) float32 1.02 1.06 ... 947.5 982.2
Attributes:
    long_name:  Number size distribution (dN/dlogDp)
    units:      count/cm^3



In [ ]:



In [23]:

    
be, bn = atmPy.aerosols.size_distribution.diameter_binning.bincenters2binsANDnames(ds.diameter_midpoint.data)



In [24]:

    
be.shape, ds.diameter_midpoint.shape









    Out[24]:





((193,), (192,))



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