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Introduction

The sizedistribution module contains various classes associated with sizedistributions:

  • SizeDist - a one dimentional size distribution
  • SizeDist_TS - size distribution time series
  • SizeDist_LS - size distribution layer series
  • OpticalProperties - optical properties derived from one of the sizedistribution classes above

All sizedistribution classes are relatives of each other with SizeDist beeing the parent. In other words SizeDist_TS and SizeDist_LS can do exactly the same things like SizeDist and a bit more.

In addition to the classes sizedistribution also contains a few usefull functions:

  • simulate_sizedistribution, simulate_sizedistribution_timeseries, and simulate_sizedistribution_layerseries - the name speaks for themself.

Imports





In [1]:



    


from atmPy.aerosols.size_distribution import sizedistribution as sd
from atmPy.general import timeseries
from atmPy.aerosols.instruments.piccolo import piccolo
from atmPy.tools import plt_tools
from atmPy.data_archives import arm
import pandas as pd
import numpy as np
%matplotlib inline
try:
    plt.style.use('hagen_default')
except:
    pass

SizeDist

Due to the similarity to SizeDist_TS and SizeDist_LS I will skip this class. It will show up every now and then below.

SizeDist_TS

simulate size distribution





In [2]:



    


dist = sd.simulate_sizedistribution_timeseries()

Load a size distribution

Usually a SizeDistribution is generated by reading experimental data using a module designed for the particular instrument (pick one from in atmPy.instruments). The following is a size distribution recorded using a POPS instrument.





In [3]:



    


dist_TS = sd.read_csv('./data/POPS_dist_TS.csv')
dist_TS._data_period = 1

dist_arm = arm.read_netCDF('../atmPy/unit_testing/test_data/sgptdmaapssizeC1.c1.20120601.004227.cdf', data_quality='bad').size_distribution



    


















    






/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py:1187: UserWarning: This function is deprecated use close_gaps instead ... unless you like thisone better
  _warnings.warn('This function is deprecated use close_gaps instead ... unless you like thisone better')

















In [13]:



    


dist_arm.plot()



    


















    
Out[13]:








(<matplotlib.figure.Figure at 0x10ce3b668>,
 <matplotlib.axes._subplots.AxesSubplot at 0x10d38d2e8>,
 <matplotlib.collections.QuadMesh at 0x10d336e48>,
 <matplotlib.colorbar.Colorbar at 0x10d519da0>)










    
























In [4]:



    


dist_TS.data.index.values



    


















    
Out[4]:








array(['2015-04-21T07:52:58.542969000', '2015-04-21T07:52:59.539062000',
       '2015-04-21T07:53:00.541016000', ...,
       '2015-04-21T10:22:08.546875000', '2015-04-21T10:22:09.542969000',
       '2015-04-21T10:22:10.546875000'], dtype='datetime64[ns]')

















In [5]:



    


dist_TS.plot()



    


















    
Out[5]:








(<matplotlib.figure.Figure at 0x1134a76d8>,
 <matplotlib.axes._subplots.AxesSubplot at 0x118b009b0>,
 <matplotlib.collections.QuadMesh at 0x118b46da0>,
 <matplotlib.colorbar.Colorbar at 0x118c7cb38>)

Perform data operation

You probably want to normalize the particle rates to particle concentrations by dividing by the flow rate. It is important not to apply operations solely to the data container. Instead perform the opperation on the entire sizedistribution instance.

e.g. POPS has a flow rate of 3cc/s ->





In [ ]:



    


dist_TS /= 3

Moment conversion

Current moment:





In [4]:



    


dist_TS.distributionType



    


















    
Out[4]:








'dNdDp'

















In [7]:



    


dist_TS.plot()



    


















    




















    
















    
Out[7]:








(<matplotlib.figure.Figure at 0x10656bd30>,
 <matplotlib.axes._subplots.AxesSubplot at 0x105337550>,
 <matplotlib.collections.QuadMesh at 0x103ac3b70>,
 <matplotlib.colorbar.Colorbar at 0x1053236d8>)

















In [10]:



    


dist_TS = dist_TS.convert2dVdlogDp()



    











In [11]:



    


dist_TS.plot()



    


















    




















    
















    
Out[11]:








(<matplotlib.figure.Figure at 0x10dafad68>,
 <matplotlib.axes._subplots.AxesSubplot at 0x10db10b00>,
 <matplotlib.collections.QuadMesh at 0x10f3247b8>,
 <matplotlib.colorbar.Colorbar at 0x10fa66748>)

Now its different:





In [52]:



    


dist_TS.distributionType



    


















    
Out[52]:








'dNdlogDp'

There are plenty more, just play with the auto-completion.

Fitting normal distribution to sizedistribution

This can take some time.





In [53]:



    


out = dist_TS.fit_normal()

See effect below in the plots

Plottin stuff

All plotting functions will return matplotlib figures and axes, so further manipulation is possible. The also have a lot of optional arguments that are worth exploring.





In [54]:



    


out = dist_TS.plot()



    


















    
























In [55]:



    


out = dist_TS.plot_particle_concentration()



    


















    
























In [56]:



    


out = dist_TS.plot_fitres()

Change time or diameter inteveral





In [57]:



    


dist_TS.get_timespan()



    


















    
Out[57]:








(Timestamp('2015-04-21 07:52:58.542969'),
 Timestamp('2015-04-21 10:22:10.546875'))

















In [58]:



    


dt = dist_TS.zoom_time(start = '2015-04-21 08:00:00.00', end = '2015-04-21 08:10:00.00')
dt = dt.zoom_diameter(start = 150, end = 600)
out = dt.plot(fit_pos=False)

Averaging over time





In [59]:



    


dt = dist_TS.average_overTime(window = '60S')
out = dt.plot(fit_pos=False)

The following will return a SizeDist instance since it is no longer a time series





In [60]:



    


avg = dist_TS.average_overAllTime()
out = avg.plot()



    


















    






/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.
  warnings.warn("Mean of empty slice.", RuntimeWarning)

Mode selection





In [21]:



    


dist_arm_N = dist_arm.convert2dNdlogDp()
dist_arm_S = dist_arm.convert2dSdlogDp()
dist_arm_V = dist_arm.convert2dVdlogDp()

f,a = plt.subplots(1,3, sharey=True)
f.set_figwidth(20)
aN = a[0]
aS = a[1]
aV = a[2]

dist_arm_N.plot(ax = aN, colorbar= False)
dist_arm_N.mode_analysis.fit_results.plot(ax = aN)
aN.set_title('dN/dlogDp')

dist_arm_S.plot(ax = aS, colorbar= False)
dist_arm_S.mode_analysis.fit_results.plot(ax = aS)
aS.set_title('dS/dlogDp')
aS.set_ylabel('')

dist_arm_V.plot(ax = aV, colorbar= False)
dist_arm_V.mode_analysis.fit_results.plot(ax = aV)
aV.set_title('dV/dlogDp')
aV.set_ylabel('')



    


















    
Out[21]:








<matplotlib.text.Text at 0x11f9e3390>

SizeDist_LS

generating a SizeDist_LS instance

Before we can generate a layerseries we need a time series that contains information about Altitude. Furthermore we want to truncate the data to the timeperiode during which a vertical profile was taken.





In [4]:



    


from atmPy.aerosols.instruments.Manta_payload import manta_payload



    











In [4]:



    


hk = piccolo.read_csv('./data/piccolo.log')
hk_II = manta_payload.read_csv('./data/Manta_raw.dat')
hk = hk.merge(hk_II)
spiral_up_start = '2015-04-21 08:20:54'
spiral_up_end = '2015-04-21 09:58:21'
dist_TS = dist_TS.zoom_time(spiral_up_start,spiral_up_end)
hk = hk.zoom_time(spiral_up_start,spiral_up_end)
hk._data_period = 1
dist_TS.housekeeping = hk



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-4-b7fcb6bdcdde> in <module>()
      1 hk = piccolo.read_csv('./data/piccolo.log')
----> 2 hk_II = manta_payload.read_csv('./data/Manta_raw.dat')
      3 hk = hk.merge(hk_II)
      4 spiral_up_start = '2015-04-21 08:20:54'
      5 spiral_up_end = '2015-04-21 09:58:21'

NameError: name 'manta_payload' is not defined

The following will generate the SizeDist_LS instance with a layer thickness of 30 meters. (force is necessary since the Altitude time series is not monotonic)





In [ ]:



    


# dist_LS = dist_TS.convert2layerseries(hk, layer_thickness= 30, force = True)
dist_LS = dist_TS.convert2verticalprofile(laythick= 30)

Plotting stuff (LS)





In [27]:



    


out = dist_LS.fit_normal()
out = dist_LS.plot()



    


















    






/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py:623: UserWarning: Size distribution is not in 'dNdlogDp'. I temporarily converted the distribution to conduct the fitting. If that is not what you want, change the code!
  "Size distribution is not in 'dNdlogDp'. I temporarily converted the distribution to conduct the fitting. If that is not what you want, change the code!")

Particle number/surface/volume/mass concentrations





In [1]:



    


from atmPy.aerosols.size_distribution import sizedistribution



    











In [50]:



    


from atmPy.general import vertical_profile



    











In [8]:



    


dt = dist_LS.reduce2temp_press_ambient()
dts = dist_LS.reduce2temp_press_standard()



    











In [11]:



    


a = dist_LS.particle_mass_concentration.plot()
dt.particle_mass_concentration.plot(ax = a)
dts.particle_mass_concentration.plot(ax=a)



    


















    
Out[11]:








<matplotlib.axes._subplots.AxesSubplot at 0x120ff7ac8>










    
























In [11]:



    


(dt.particle_mass_concentration.data / dist_LS.particle_mass_concentration.data).plot()



    


















    
Out[11]:








<matplotlib.axes._subplots.AxesSubplot at 0x1147dbba8>










    
























In [18]:



    


((dist_LS.housekeeping.data.Temperature_instrument + 273.15) / (dist_LS.housekeeping.data.Temperature +273.15)).plot()



    


















    
Out[18]:








<matplotlib.axes._subplots.AxesSubplot at 0x1141bd828>










    
























In [14]:



    


a = dist_LS.housekeeping.data.Temperature.plot()
dist_LS.housekeeping.data.Temperature_instrument.plot(ax = a)



    


















    
Out[14]:








<matplotlib.axes._subplots.AxesSubplot at 0x113cf2160>










    
























In [ ]:



    






    











In [ ]:



    






    











In [20]:



    


dist = dist_LS.copy()



    











In [47]:



    


data = dist.data.iloc[0:5, :]
press_is = dist.housekeeping.data.iloc[0:5]['Pressure_Pa']
tmp_is = dist.housekeeping.data.iloc[0:5]['Temperature_instrument'] + 273.15

press_shall = dist.housekeeping.data.iloc[0:5]['Pressure_Pa']
tmp_shall = dist.housekeeping.data.iloc[0:5]['Temperature'] + 273.15



    











In [21]:



    


data = dist.data
press_is = dist.housekeeping.data['Pressure_Pa']
tmp_is = dist.housekeeping.data['Temperature_instrument'] + 273.15

press_shall = 1000
tmp_shall = 273.15

newdata = data.mul(tmp_is, axis = 0).truediv(press_is, axis=0).truediv(tmp_shall, axis = 0).mul(press_shall, axis = 0)



    











In [22]:



    


dist.data = newdata



    











In [23]:



    


dist.plot()



    


















    
Out[23]:








(<matplotlib.figure.Figure at 0x114244630>,
 <matplotlib.axes._subplots.AxesSubplot at 0x11428fda0>,
 <matplotlib.collections.QuadMesh at 0x1143b02e8>,
 <matplotlib.colorbar.Colorbar at 0x1144c02b0>)










    
























In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [16]:



    


dt.properties.particle_density = 1



    











In [19]:



    


dist_LS.properties.particle_density = 20



    











In [20]:



    


a = dist_LS.particle_mass_concentration.plot()
dt.particle_mass_concentration.plot(ax = a)



    


















    
Out[20]:








<matplotlib.axes._subplots.AxesSubplot at 0x10ea472b0>










    
























In [19]:



    


bla = dist_LS.optical_properties_settings.refractive_index._dict['value']



    











In [21]:



    


bla.__bool__()



    


















    
Out[21]:








True

















In [ ]:



    






    











In [12]:



    


from atmPy import atmosphere



    











In [25]:



    


reload(atmosphere)
reload(atmosphere.atmosphere)



    


















    
Out[25]:








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

















In [21]:



    


for i in dist_LS.housekeeping.data.columns:
    print(i)



    


















    







ABSFLOW
ABSFP
ABSLED
ABSRA
AFR
AFRcomp
AGL
AGLGood
AP_Mode
AX_Bias
AY_Bias
AZ_Bias
AccelGood
AckRatio
AeroTerm
Alt
Altitude
AttGood
BATTV
BaroGood
Boundary
BoxTemp
Brakes
CHEMFLOW
CHMFL
CHT_A
CHT_B
CPUFail
CommTimeout
Deadman
Direction
Drop
Dynamic
ECU_ERR_0
ECU_ERR_1
EGT_A
EGT_B
EngKill
EngineTime
ExtADC0
ExtADC1
ExtADC2
ExtADC3
FirstStageFail
FiveAFail
FiveDFail
FlightTerm
FlightTimer
FuelPress
GPSFail
GPSITOW
GPSTimeout
GPSWeek
GSDirection
GSGroundSpeed
GSHeight
GSLat
GSLon
GSPilotPrcnt
GSPilotRate
GSVDown
GSVEast
GSVNorth
GS_RSSI
GroundSpeed
GyroGood
IAT
InjectAngle
InjectTime
InputC
InputV
Lat
LeftRPM
Lights
Lon
LoopStatus6
LoopStatus7
LoopTarget0
LoopTarget1
LoopTarget2
LoopTarget3
LoopTarget4
LoopTarget5
MAP
MA_Mode
MBAlt
MBBelow
MBCross
MBDown
MBETA
MBEast
MBHead
MBLat
MBLon
MBNorth
MBSolnType
MBTime
MagBiasGood
MagGood
MagHdg
MagX
MagY
MagZ
OAT
OPCB4
OPCB5
OPCB6
OPCB7
OPCFL
OPCFLOW
Orbit
P
P_Bias
Parachute
PilotPrcnt
PilotRate
Pitch
PosGood
Pressure_Pa
Q
Q_Bias
R
RSSI
R_Bias
Relative_humidity
RightRPM
Roll
SAMPFLOW
SATBT
SATFL
SATFLOW
SATTT
ServoC
ServoV
Static
Surface0
Surface1
Surface10
Surface11
Surface12
Surface13
Surface14
Surface15
Surface2
Surface3
Surface4
Surface5
Surface6
Surface7
Surface8
Surface9
TAS
TASGood
TPS
Temperature
Temperature_instrument
Track_VX
Track_VY
Track_VZ
Track_X
Track_Y
Track_Z
TrackerTarget
UAStime
VDown
VEast
VNorth
VelGood
Volts
WindGood
WindSouth
WindWest
Xaccel
Yaccel
Yaw
YawGood
Zaccel

















In [22]:



    


dist_LS.housekeeping.data.Pressure_Pa



    


















    
Out[22]:








68.0      1004.718054
98.0      1000.934414
128.0      997.448625
158.0      993.516933
188.0      989.653760
218.0      985.943403
248.0      981.928054
278.0      977.876513
308.0      974.214812
338.0      970.260960
368.0      966.881854
398.0      963.037267
428.0      959.310355
458.0      955.710030
488.0      951.954669
518.0      948.455896
548.0      944.791626
578.0      940.906873
608.0      937.135610
638.0      933.436374
668.0      930.074462
698.0      926.306921
728.0      922.805120
758.0      919.445278
788.0      915.591942
818.0      911.777140
848.0      908.157903
878.0      904.617622
908.0      901.191565
938.0      897.721062
             ...     
2138.0     766.752648
2168.0     763.646073
2198.0     760.654395
2228.0     757.626947
2258.0     754.602653
2288.0     751.362183
2318.0     748.566789
2348.0     745.571909
2378.0     742.815047
2408.0     739.618663
2438.0     736.661990
2468.0     733.891398
2498.0     731.092780
2528.0     728.325119
2558.0     725.502806
2588.0     722.508083
2618.0     719.558020
2648.0     716.678101
2678.0     713.850116
2708.0     711.202331
2738.0     708.173760
2768.0     705.428570
2798.0     702.550108
2828.0     699.885904
2858.0     696.908482
2888.0     694.192579
2918.0     691.404384
2948.0     688.657564
2978.0     685.816147
3008.0     683.486839
Name: Pressure_Pa, dtype: float64

















In [18]:



    


dist_LS.particle_volume_concentration.data



    


















    
Out[18]:










  
    

      
      volume concentration $\mu m^3 / cm^{3}$
    

  
  
    

      68.0
      3.681984
    

    

      98.0
      3.659348
    

    

      128.0
      3.716356
    

    

      158.0
      3.806208
    

    

      188.0
      3.690323
    

    

      218.0
      3.700879
    

    

      248.0
      3.786778
    

    

      278.0
      3.595428
    

    

      308.0
      3.519840
    

    

      338.0
      3.440107
    

    

      368.0
      3.378525
    

    

      398.0
      3.538174
    

    

      428.0
      3.524586
    

    

      458.0
      3.450349
    

    

      488.0
      3.362613
    

    

      518.0
      3.583933
    

    

      548.0
      3.316558
    

    

      578.0
      3.346804
    

    

      608.0
      3.434439
    

    

      638.0
      3.618146
    

    

      668.0
      3.576974
    

    

      698.0
      3.373965
    

    

      728.0
      3.607334
    

    

      758.0
      3.507051
    

    

      788.0
      3.587794
    

    

      818.0
      3.537112
    

    

      848.0
      3.582445
    

    

      878.0
      3.552355
    

    

      908.0
      3.780381
    

    

      938.0
      3.560908
    

    

      ...
      ...
    

    

      2138.0
      0.669815
    

    

      2168.0
      0.493437
    

    

      2198.0
      0.387231
    

    

      2228.0
      0.526640
    

    

      2258.0
      0.411520
    

    

      2288.0
      0.535123
    

    

      2318.0
      0.773995
    

    

      2348.0
      0.745659
    

    

      2378.0
      0.769211
    

    

      2408.0
      0.683971
    

    

      2438.0
      0.367498
    

    

      2468.0
      0.167283
    

    

      2498.0
      0.104162
    

    

      2528.0
      0.082084
    

    

      2558.0
      0.115201
    

    

      2588.0
      0.061557
    

    

      2618.0
      0.104313
    

    

      2648.0
      0.142245
    

    

      2678.0
      0.115331
    

    

      2708.0
      0.089424
    

    

      2738.0
      0.114910
    

    

      2768.0
      0.106805
    

    

      2798.0
      0.142890
    

    

      2828.0
      0.163728
    

    

      2858.0
      0.198273
    

    

      2888.0
      0.210405
    

    

      2918.0
      0.315223
    

    

      2948.0
      0.345617
    

    

      2978.0
      0.358154
    

    

      3008.0
      0.343782
    

  



99 rows × 1 columns



















In [33]:



    


dist_LS.particle_volume_concentration.data[dist_LS.particle_volume_concentration.data.columns[0]] * dist_LS.housekeeping.data.Pressure_Pa.values



    


















    
Out[33]:








68.0      3699.355917
98.0      3662.767077
128.0     3706.874467
158.0     3781.531626
188.0     3652.142440
218.0     3648.856837
248.0     3718.343935
278.0     3515.884448
308.0     3429.080299
338.0     3337.801208
368.0     3266.634278
398.0     3407.393353
428.0     3381.172286
458.0     3297.532763
488.0     3201.055000
518.0     3399.202321
548.0     3133.456449
578.0     3149.030890
608.0     3218.535499
638.0     3377.309023
668.0     3326.851922
698.0     3125.327177
728.0     3328.866445
758.0     3224.541750
788.0     3284.955023
818.0     3225.057442
848.0     3253.425454
878.0     3213.522599
908.0     3406.847437
938.0     3196.702210
             ...     
2138.0     513.582266
2168.0     376.810913
2198.0     294.549241
2228.0     398.996823
2258.0     310.533740
2288.0     402.071181
2318.0     579.386680
2348.0     555.942445
2378.0     571.381348
2408.0     505.878054
2438.0     270.722059
2468.0     122.767706
2498.0      76.152374
2528.0      59.783738
2558.0      83.578985
2588.0      44.475407
2618.0      75.059258
2648.0     101.943757
2678.0      82.329318
2708.0      63.598719
2738.0      81.376177
2768.0      75.343281
2798.0     100.387277
2828.0     114.591128
2858.0     138.178154
2888.0     146.061690
2918.0     217.946891
2948.0     238.011685
2978.0     245.628120
3008.0     234.970598
Name: volume concentration $\mu m^3 / cm^{3}$, dtype: float64

















In [45]:



    


data = dist_LS.particle_volume_concentration.data[dist_LS.particle_volume_concentration.data.columns[0]]
data



    


















    
Out[45]:








68.0      3.681984
98.0      3.659348
128.0     3.716356
158.0     3.806208
188.0     3.690323
218.0     3.700879
248.0     3.786778
278.0     3.595428
308.0     3.519840
338.0     3.440107
368.0     3.378525
398.0     3.538174
428.0     3.524586
458.0     3.450349
488.0     3.362613
518.0     3.583933
548.0     3.316558
578.0     3.346804
608.0     3.434439
638.0     3.618146
668.0     3.576974
698.0     3.373965
728.0     3.607334
758.0     3.507051
788.0     3.587794
818.0     3.537112
848.0     3.582445
878.0     3.552355
908.0     3.780381
938.0     3.560908
            ...   
2138.0    0.669815
2168.0    0.493437
2198.0    0.387231
2228.0    0.526640
2258.0    0.411520
2288.0    0.535123
2318.0    0.773995
2348.0    0.745659
2378.0    0.769211
2408.0    0.683971
2438.0    0.367498
2468.0    0.167283
2498.0    0.104162
2528.0    0.082084
2558.0    0.115201
2588.0    0.061557
2618.0    0.104313
2648.0    0.142245
2678.0    0.115331
2708.0    0.089424
2738.0    0.114910
2768.0    0.106805
2798.0    0.142890
2828.0    0.163728
2858.0    0.198273
2888.0    0.210405
2918.0    0.315223
2948.0    0.345617
2978.0    0.358154
3008.0    0.343782
Name: volume concentration $\mu m^3 / cm^{3}$, dtype: float64

















In [51]:



    


dist_LS.physical_property_density = 2



    











In [48]:



    


bla = atmosphere.normalize2standard_pressure_and_temperature(dist_LS.particle_volume_concentration.data.iloc[:,0], 
                                                             dist_LS.housekeeping.data.Pressure_Pa, 
                                                             dist_LS.housekeeping.data.Temperature_instrument + 273.15)



    











In [42]:



    


a = dist_LS.particle_volume_concentration.plot()
a.plot(bla.values, bla.index)



    


















    
Out[42]:








[<matplotlib.lines.Line2D at 0x118d44da0>]

hygroscopic growth

Hygroscopic growth can either be applied with one RH value or by using the 'Relative_humidity' column in the associated housekeeping file. We made sure that this column exist by merging the file from the manta aircraft earlier. Since the index of refraction is adjusted during the growth it is nesecarry to define that value before proceeding.





In [8]:



    


dist_LS.hygroscopic_growth_settings.refractive_index = 1.455 # refractive index of the calibration material DOS
dist_LS_grown = dist_LS.apply_hygro_growth(0.5)
# out = dist_LS_grown.plot(fit_pos=False)



    











In [14]:



    


dist_LS_grown.optical_properties_settings



    


















    






---------------------------------------------------------------------------
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    382                                 return meth(obj, self, cycle)
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    384         finally:
    385             self.end_group()

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/opt/local/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
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    703         if idx:

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    251 
    252     def __repr__(self):
--> 253         out = _all_attributes2string(self)
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/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in _all_attributes2string(obj)
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    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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    189 
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    192 
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    189 
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--> 191         return str(self._dict['value'])
    192 
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    189 
    190     def __repr__(self):
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    192 
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    189 
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    192 
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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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    189 
    190     def __repr__(self):
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    192 
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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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    189 
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--> 191         return str(self._dict['value'])
    192 
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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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    189 
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--> 191         return str(self._dict['value'])
    192 
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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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    189 
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    192 
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    189 
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    189 
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    192 
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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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    189 
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--> 191         return str(self._dict['value'])
    192 
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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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    189 
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    189 
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--> 191         return str(self._dict['value'])
    192 
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    189 
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--> 191         return str(self._dict['value'])
    192 
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    189 
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--> 191         return str(self._dict['value'])
    192 
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    189 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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    189 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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    192 
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    192 
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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    192 
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
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--> 191         return str(self._dict['value'])
    192 
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    192 
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    192 
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    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
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--> 191         return str(self._dict['value'])
    192 
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    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

/Users/htelg/prog/atm-py/atmPy/aerosols/size_distribution/sizedistribution.py in __repr__(self)
    189 
    190     def __repr__(self):
--> 191         return str(self._dict['value'])
    192 
    193     def __bool__(self):

RecursionError: maximum recursion depth exceeded
















In [34]:



    


dist_LS_grown.plot()



    


















    
Out[34]:








(<matplotlib.figure.Figure at 0x1177ff518>,
 <matplotlib.axes._subplots.AxesSubplot at 0x1178d5860>,
 <matplotlib.collections.QuadMesh at 0x1176be3c8>,
 <matplotlib.colorbar.Colorbar at 0x116f443c8>)

Optical properties

generating OpticalProperties instance

To calculate optical properties it is again necessary to specify the refractive index, which we already did earlier.





In [13]:



    


dist_LS.optical_properties_settings.refractive_index = 1.455
dist_LS.optical_properties_settings.wavelength = 550.4
opt_prop = dist_LS.optical_properties



    











In [14]:



    


dist_LS.optical_properties.extinction_coeff.plot()



    


















    
Out[14]:








<matplotlib.axes._subplots.AxesSubplot at 0x119c43b70>










    
























In [10]:



    


dist_LS.optical_properties.extinction_coeff.plot()



    


















    
Out[10]:








<matplotlib.axes._subplots.AxesSubplot at 0x111f16780>










    
























In [10]:



    


dist_LS.optical_properties.extinction_coeff.plot()



    


















    
Out[10]:








<matplotlib.axes._subplots.AxesSubplot at 0x111f16780>










    
























In [10]:



    


dist_LS.optical_properties.extinction_coeff.plot()



    


















    
Out[10]:








<matplotlib.axes._subplots.AxesSubplot at 0x111f16780>










    
























In [9]:



    


dist_LS.optical_properties.asymmetry_param_VP.plot()



    


















    
Out[9]:








<matplotlib.axes._subplots.AxesSubplot at 0x11750cc50>










    
























In [10]:



    


dist_LS.optical_properties.aerosol_optical_depth_cumulative_VP.plot()



    


















    
Out[10]:








<matplotlib.axes._subplots.AxesSubplot at 0x10b6ba780>










    
























In [10]:



    






    


















    






---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-10-90d0a3829762> in <module>()
----> 1 dist_LS.optical_properties.extinction_coeff

/Users/htelg/prog/atm-py/atmPy/aerosols/physics/optical_properties.py in extinction_coeff(self)
    305                 self.__extinction_coeff_sum_along_d = df
    306             else:
--> 307                 raise TypeError('not possible for this distribution type')
    308             self.__extinction_coeff_sum_along_d._data_period = self._data_period
    309         return self.__extinction_coeff_sum_along_d

TypeError: not possible for this distribution type
















In [ ]:

Content source: hagne/atm-py

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