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], [symbols_x, symbols_y], filter)
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. |
[symbols_x, symbols_y] |
numpy.ndarray | Definition of symbols_* can be found here pypho_arbmod. |
filter |
list | Optical filter of the coherent receiver. Defined by pypho_optfi. |
| 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, filter, [symbols_x, symbols_y])
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. |
filter |
list | Optical filter of the coherent receiver. Defined by pypho_optfi. |
[symbols_x, symbols_y] |
numpy.ndarray | Definition of symbols_* can be found here pypho_arbmod. |
| 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 $\lg{\left(BER_y\right)}$ |