

In [1]:

    
from atmPy import sizedistribution



In [2]:

    
%matplotlib inline

size distribution

simulate a sizedistribution



In [87]:

    
sd = sizedistribution.simulate_sizedistribution(diameter=[10, 2500],
                                                numberOfDiameters=100, 
                                                centerOfAerosolMode=200, 
                                                widthOfAerosolMode=0.2, 
                                                numberOfParticsInMode=1000)
f,a = sd.plot_distribution()

convert size distribution between representations



In [4]:

    
sd = sd.convert2dNdlogDp() #this is not an inplace operation. It returns a new object
f,a = sd.plot_distribution()

save and read a sizedistribution



In [8]:

    
sd.save_csv('data/size_distribution.cvs')



In [9]:

    
unsave = sizedistribution.read_distribution_csv('data/size_distribution.cvs')
f,a = unsave.plot_distribution()

size distribution time series

simulate sizedistribution time series



In [23]:

    
sdtsf = sizedistribution.simulate_sizedistribution_timeseries()
sdtsf = sdtsf.convert2dNdlogDp()
f,a,pc,cb = sdtsf.plot_distribution(removeTickLabels=['50', '70', '90', '500','700', '900'])

zoom in

The following procedure will actually truncate your data. This is usefull if your data volume is very big. And it is necessary when you want to create a single sizedistribution instance from this particular stretch of time (see below)



In [24]:

    
sdtsf.get_timespan()









    Out[24]:





(Timestamp('2014-11-24 17:00:00', offset='10S'),
 Timestamp('2014-11-24 17:59:50', offset='10S'))



In [25]:

    
sdtsfZ = sdtsf.zoom_time('2014-11-24 17:10:00','2014-11-24 17:20:00')
sdtsfZ.plot_distribution()









    Out[25]:





(<matplotlib.figure.Figure at 0x10e400110>,
 <matplotlib.axes._subplots.AxesSubplot at 0x1136a43d0>,
 <matplotlib.collections.QuadMesh at 0x114142a10>,
 <matplotlib.colorbar.Colorbar instance at 0x1140f57e8>)

change the resolution



In [35]:

    
sdtsfL = sdtsf.average_overTime('60S')
f,a,pc,cb = sdtsfL.plot_distribution()

average over all time



In [36]:

    
sd = sdtsf.average_overAllTime()



In [37]:

    
sd.plot_distribution()









    Out[37]:





(<matplotlib.figure.Figure at 0x113ab76d0>,
 <matplotlib.axes._subplots.AxesSubplot at 0x11489bb10>)

size distribution layer series



In [129]:

    
sdls = sizedistribution.simulate_sizedistribution_layerseries()
f,a,pc,cb = sdls.plot_distribution()









    



[    0.    60.   120.   180.   240.   300.   360.   420.   480.   540.
   600.   660.   720.   780.   840.   900.   960.  1020.  1080.  1140.
  1200.  1260.  1320.  1380.  1440.  1500.  1560.  1620.  1680.  1740.
  1800.  1860.  1920.  1980.  2040.  2100.  2160.  2220.  2280.  2340.
  2400.  2460.  2520.  2580.  2640.  2700.  2760.  2820.  2880.  2940.
  3000.  3060.  3120.  3180.  3240.  3300.  3360.  3420.  3480.  3540.
  3600.  3660.  3720.  3780.  3840.  3900.  3960.  4020.  4080.  4140.
  4200.  4260.  4320.  4380.  4440.  4500.  4560.  4620.  4680.  4740.
  4800.  4860.  4920.  4980.  5040.  5100.  5160.  5220.  5280.  5340.
  5400.  5460.  5520.  5580.  5640.  5700.  5760.  5820.  5880.  5940.
  6000.]



In [273]:

    
reload(sizedistribution)









    Out[273]:





<module 'atmPy.sizedistribution' from '/Users/htelg/prog/atm-py/atmPy/sizedistribution.py'>

add an aerosol layer



In [321]:

    
sdls = sizedistribution.aerosolSizeDistribution_layerseries(None, sd.bins, sd.distributionType, None)

sd = sizedistribution.simulate_sizedistribution(centerOfAerosolMode=100)
lb = [0,10]
sdls.add_layer(sd, lb)

sd = sizedistribution.simulate_sizedistribution(centerOfAerosolMode=200)
lb = [10,20]
sdls.add_layer(sd, lb)

sd = sizedistribution.simulate_sizedistribution(centerOfAerosolMode=300)
lb = [20,30]
sdls.add_layer(sd, lb)
sd = sizedistribution.simulate_sizedistribution(centerOfAerosolMode=400)
lb = [30,40]
sdls.add_layer(sd, lb)



In [322]:

    
sdls = sdls.convert2dNdlogDp()
sdls.plot_eachLayer()









    Out[322]:





(<matplotlib.figure.Figure at 0x118cacc10>,
 <matplotlib.axes._subplots.AxesSubplot at 0x119477c10>)

calculate AOD

ACTION required:

what to do with gaps in the layers when clauclating the AOD?!?
testing!!!

create a aerosol layer instance and put some layers in



In [417]:

    
sdls = sizedistribution.aerosolSizeDistribution_layerseries(None, sd.bins, sd.distributionType, None)

sd = sizedistribution.simulate_sizedistribution(centerOfAerosolMode=400)
lb = [0,10]
sdls.add_layer(sd, lb)

sd = sizedistribution.simulate_sizedistribution(centerOfAerosolMode=200)
lb = [10,20]
sdls.add_layer(sd, lb)

sd = sizedistribution.simulate_sizedistribution(centerOfAerosolMode=300)
lb = [20,30]
sdls.add_layer(sd, lb)
sd = sizedistribution.simulate_sizedistribution(centerOfAerosolMode=400)
lb = [30,40]
sdls.add_layer(sd, lb)

calculate AOD



In [418]:

    
AOD = sdls.calculate_AOD()



In [419]:

    
AOD.keys()









    Out[419]:





['AOD', 'extCoeffPerLayer', 'AOD_layer']



In [420]:

    
AOD['AOD']









    Out[420]:





0.016044878050970806



In [421]:

    
AOD['AOD_layer'].plot()









    Out[421]:





<matplotlib.axes._subplots.AxesSubplot at 0x11aaa0fd0>



In [424]:

    
for e,lay in enumerate(AOD['extCoeffPerLayer'].values):
    plt.plot(sdls.bincenters,lay)
plt.semilogx()









    Out[424]:





[]