pypho v1

pypho_functions

pypho_functions is a collection of usefull functions to make your coding live easier.

 

dbm2w( power_dbm )

Converts a power value from dBm to W: $P|_{W} = 10^{\frac{P|_{dBm}}{10}}$ W

Parameter	Type	Description
power_dbm	float	Power value in dBm ($P|_{dBm}$).

 

Output	Type	Description
Power value in W	float	$P|_{W}$

 

 

w2dbm( power_w )

Converts a power value from W to dBm: $P|_{dBm} = 10\cdot \lg{\left(\frac{P|_{W}}{1mW}\right)}$ dBm.

Parameter	Type	Description
power_dbm	float	Power value in W ($P|_{W}$).

 

Output	Type	Description
Power value in dBm	float	$P|_{dBm}$

 

 

getpower_W( E )

Calculates the mean power in W of an optical Signal $E$: $P|_{dBm} = \frac{1}{T} \int _{ \tau=0 }^{ T } \left( \left\lvert E_x(\tau) \right\rvert^{ 2 } +\left\lvert E_y(\tau) \right\rvert^{ 2 }\right) d\tau $.

Parameter	Type	Description
E	numpy.ndarray	2 dimensional array with complex values, which represents the sample points over time of the X- and Y-polarisation. For example E[0]['E']. Read here about data signal and noise representation

 

Output	Type	Description
Power value in W	float	$P|_{W}$

 

 

getpower_dBm( E )

Same as getpower_w( E ), but output is in dBm.

 

 

Dec_x, Esx_I_ax, Esx_Q_ax, Dec_y, Esy_I_ax, Esy_Q_ax = ...
get_decision_matrix( gp, E, [constpts_x, constpts_y], [bits_x, bits_y], LO, filter, sigsampler)

Calculates a decision matrix needed for the BER calculation.

Parameter	Type	Description
gp	numpy.ndarray	Global parameters as defined in pypho_setup.
E	list	List which defines the optical data and noise signal. Read here about data signal and noise representation.
[constpts_x, constpts_y]	numpy.ndarray	Defines the constellation points. Definition of constpoints can be found here pypho_arbmod.
[bits_x, bits_y]	numpy.ndarray	Definition of bits_* can be found here pypho_arbmod.
LO	list	Local oscillator of the coherent receiver. cw-laser defined by pypho_cwlaser.
filter	list	Optical filter of the coherent receiver. Defined by pypho_optfi.
sigsampler	list	Signal sampler defined by pypho_sample.

 

Output	Type	Description
Dec_x, Dec_y	numpy.ndarray	Array with the identification numbers of each constelleation point as defined in [constpts_x, constpts_y] (pypho_arbmod).
Esx_I_ax, Esx_Q_ax, Esy_I_ax, Esy_Q_ax	numpy.ndarray	Arrays representing the axis of the I- and Q- part of the received signal of the X- and Y-polarisation.

 

You can find an example for the BER calculation including the calculation of the decision matrix here.

 

 

BER = ...
calc_BER( gp, E, [constpts_x, constpts_y], OSNR, Dec_x, Dec_y, Esx_I_ax, Esx_Q_ax, Esy_I_ax, Esy_Q_ax, M, LO, filter, sigsampler)

Calculates a decision matrix needed for the BER calculation.

Parameter	Type	Description
gp	numpy.ndarray	Global parameters as defined in pypho_setup.
E	list	List which defines the optical data and noise signal. Read here about data signal and noise representation.
[constpts_x, constpts_y]	numpy.ndarray	Defines the constellation points. Definition of constpoints can be found here pypho_arbmod.
OSNR	float	OSNR in dB
Dec_x, Dec_y	numpy.ndarray	Array with the identification numbers of each constellation point as defined in [constpts_x, constpts_y] (pypho_arbmod).
Esx_I_ax, Esx_Q_ax, Esy_I_ax, Esy_Q_ax	numpy.ndarray	Arrays representing the axis of the I- and Q- part of the received signal of the X- and Y-polarisation.
M	inetger	Defines the number of repitions. This parameters defines the lowest BER, which can be theoretical achieved.
LO	list	Local oscillator of the coherent receiver. cw-laser defined by pypho_cwlaser.
filter	list	Optical filter of the coherent receiver. Defined by pypho_optfi.
sigsampler	list	Signal sampler defined by pypho_sample.

 

Output	Type	Description
BER	numpy.ndarray	Array with two float numbers representing the BER of the X- and Y-polaristion: $\lg{\left(BER_x\right)}$ and $\lg{\left(BER_y\right)}$

You can find an example for the BER calculation including the calculation of the decision matrix here.