Load an existing random_stack object from file. It was created with 60 layers, with length variation of 10 micron +/- 60%. Create an incident_wp instance which is our pump pulse. The add_packet() function conveniently adds a Gaussian shaped spectrum in the 1350-1450nm wavelength range, consisting of 200 eigenmodes.



In [1]:

    
from stack import *
my_stack = load('io_files/saved_stack.txt')
my_pump = incident_wp(my_stack); my_pump.add_packet(200,[1350,1450])

We can initialize the pump pulse to be outside the stack, by focusing all positive-wave components of eigenmodes in layer 0



In [2]:

    
my_pump = pulse_shape(my_pump,focus_mask(0,0,my_pump))
my_pump.plot_field(1,1000)

We can also intialize the pump such that the positive-wave components are focused in an arbitrary layer (e.g.40)



In [3]:

    
example_pump = pulse_shape(my_pump,focus_mask(40,0,my_pump))
example_pump.plot_field(1,500)

"Advancing" the wavepacket with a negative value would propagate it backward in time. Time-propagation is equivalent to linear phase shaping, hence does not change 1- and 2-photon power spectra, but changes the spectral phases



In [4]:

    
example_pump.advance(-2000)
example_pump.plot_field(1,500)

The following shows some data of the population inversion, calculated as the total 2-photon power across all frequencies in each layer.

Each title of the plot shows the type of shaping done for the pump pulse, and the power ratio between layers 1 to 20, to layers 30-50.

Shaping types:

"no shaping": the pulse is focused outside (left side) the stack
"positive-wave focusing": make the 'a_j' complex amplitudes to have the same phase for all eigenmodes at midpoint of the layer
"total field focusing": make the total complex field to have the same phase for all eigenmodes, at a location with maximum spatial field magnitude
"minimization": randomize phases of the eigenmodes which do not localize in layers we'd like to focus (e.g. randomize those in layer 1 to 20, but not 40)



In [7]:

    
files = ['io_files/dataframe-TPS-focus0.txt',
         'io_files/dataframe-TPS-focus40.txt',
         'io_files/dataframe-TPS-focus40-2.txt',
         'io_files/dataframe-TPS-focus40+rand.txt',
         'io_files/dataframe-TPS-rand.txt']

titles = ['no shaping: ',
          'positive-wave focusing: ',
          'total field focusing: ',
          'positive-wave + minimization:',
          'minimization only: ']

data = map(lambda filename: pandas.read_csv(filename,index_col=0),files)

inv = map(lambda df: map(lambda j: sum(df.loc[j+1]),range(59)),data)

for j in range(len(inv)):
    map(lambda k:my_stack.add_inversion(k+1,inv[j][k]),range(59))
    title(titles[j]+"{:7.4f}".format(sum(inv[j][1:20])/sum(inv[j][30:50])),fontsize=18)
    my_stack.plot(show_inversion=True,inv_max=2.5)
    my_stack.clear_inversion()



In [8]:

    
for j in range(len(inv)):
    pyplot.plot(inv[j],'o-')

pyplot.legend(titles,fontsize=12)
pyplot.show()



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