SpectrumAnalyzer

class ipkiss3.all.SpectrumAnalyzer

Tool to analyze the transmission spectra of an S-matrix.

Finds and stores passband (or stopband) information and provides measurement methods.

Parameters:

smatrix: SMatrix1DSweep: The smatrix sweep object
input_port_mode: str: The input port or port-mode. e.g. “in1”, “out1:0”
output_port_modes: List[str]: The output ports or port-modes e.g. [“out0”, “out1”, “out2”]
dB: bool: Whether to analyze the smatrix power in decibel (True) or linear scale (False)
peak_method: ‘spline’ or ‘cwt’: Peak detection method to be used. Please check i3.find_peaks for a detailed description of the available methods.
peak_threshold: float or None: Threshold for peak detection
bandpass: bool or None: Treat spectra as bandpass (True), bandstop (False) or automatic (None)

visualize(show=True, title=None, figure=None): Visualize the transmission spectra.

peaks()

Return the peaks per channel.

the peak data is stored as numpy record arrays. This allows you to use strings to access the values:

analyzer.peaks()['out1']['power']

Will give you only the power values of the peaks. Using ‘wavelength’ gives you the frequencies where the peaks are located.

analyzer.peaks()[‘out1’][‘wavelength’]

Returns:

dict

A mapping from the output ports to their corresponding spectral peaks:

{'out1': [(peak1_x, peak1_power), (peak2_x, peak2_power)]}

Notes

The first index of the record array takes its value from the sweep_parameter_name of the smatrix-sweep. In most cases this will be ‘wavelength’ as used in the example above.

find_peaks(method='spline', threshold=None, bandpass=None, store=True, smoothing=0)

Uses i3.find_peaks to calculate a new list of peaks for every channel.

Parameters:

method: ‘spline’ or ‘cwt’: Peak finding method
threshold: float or None: Threshold power for peak finding. (autodetect if None)
bandpass: True or None: Interprete spectra as bandpass (True), bandstop (False) or autodetect (None)
store: bool: True: store the found peaks on the spectra. False: don’t store

Returns:

dict

A mapping from the output ports to their corresponding spectral peaks:

{'out1': [(peak1_x, peak1_power), (peak2_x, peak2_power)]}

trim(band=None)

Trim the channels over a wavelength range of interest.

Parameters:

band: Lower and upper bound of x for the channels, i.e. (x_min, x_max). If no band is given, the whole spectrum is taken into account.

Returns:

SpectrumAnalyzer: New instance of SpectrumAnalyzer with trimmed channels

min_insertion_losses(bands=None)

Calculate the minimum insertion losses for every channel.

Parameters:

bands

Lower and upper bounds of x for the channels. If not specified, the whole spectrum will be used. Can be specified manually or calculated by means of cutoff_passbands or width_passbands.

Example where channel ‘out1’ has two passbands and ‘out2’ one passband over which we want to calculate the insertion loss:

{'out1': [(x_min1, x_max1), (x_min2, x_max2)], 'out2': [(x_min3, x_max3)]}

Returns:

dict

A mapping from the output ports to their corresponding insertion losses (one per band):

{'out1': [-3.134, -3.132], 'out2': [-3.024]}

max_insertion_losses(bands=None)

Calculate the maximum insertion losses for every channel.

Parameters:

bands

Lower and upper bounds of x for the channels. If not specified, the whole spectrum will be used. Can be specified manually or calculated by means of cutoff_passbands or width_passbands.

Example where channel ‘out1’ has two passbands and ‘out2’ one passband over which we want to calculate the insertion loss:

{'out1': [(x_min1, x_max1), (x_min2, x_max2)], 'out2': [(x_min3, x_max3)]}

Returns:

dict

A mapping from the output ports to their corresponding insertion losses (one per band):

{'out1': [-3.134, -3.132], 'out2': [-3.024]}

fsr()

Calculate the free spectral range for every channel.

Returns:

dict

A mapping from the output ports to their corresponding FSR:

{'out1': 0.020, 'out2': 0.021}

width_passbands(width)

Calculate x-limited passbands centered around the peaks for every channel.

Parameters:

width: float: Width of one passband, expressed in the same units and scale as x.

Returns:

dict

A mapping from the output ports to their corresponding passbands:

{'out1': [(x1, x2), (x3, x4)], 'out2': [(x5, x6)]}

cutoff_passbands(cutoff)

Calculate power-limited passbands centered around the peaks for every channel.

Parameters:

cutoff: float: Maximum acceptable power loss with respect to the peak power.

Returns:

dict

A mapping from the output ports to their corresponding passbands:

{'out1': [(x1, x2), (x3, x4)], 'out2': [(x5, x6)]}

far_crosstalk(bands)

Calculate the maximum crosstalk for every channel, ignoring nearest neighbours.

Parameters:

bands

Lower and upper bounds of x for the channels. Can be specified manually or calculated by means of cutoff_passbands or width_passbands. Example where channel ‘out1’ has two passbands and ‘out2’ one passband over which we want to calculate the insertion loss:

{'out1': [(x_min1, x_max1), (x_min2, x_max2)], 'out2': [(x_min3, x_max3)]}

Returns:

dict

A mapping from the output ports to their corresponding maximum crosstalks, ignoring nearest neighbours:

{'out1': -80.3, 'out2': -79.1}

near_crosstalk(bands)

Calculate the maximum crosstalk for every channel, taking only nearest neighbours into account.

Parameters:

bands

Lower and upper bounds of x for the channels. Can be specified manually or calculated by means of cutoff_passbands or width_passbands.

Example where channel ‘out1’ has two passbands and ‘out2’ one passband over which we want to calculate the insertion loss:

{'out1': [(x_min1, x_max1), (x_min2, x_max2)], 'out2': [(x_min3, x_max3)]}

Returns:

dict

A mapping from the output ports to their corresponding maximum crosstalks, taking only nearest neighbours into account:

{'out1': -80.3, 'out2': -79.1}

far_crosstalk_matrix(bands)

Calculate the crosstalk for every passband in every channel, ignoring nearest neighbours.

Parameters:

bands

Lower and upper bounds of x for the channels. Can be specified manually or calculated by means of cutoff_passbands or width_passbands.

Example where channel ‘out1’ has two passbands and ‘out2’ one passband over which we want to calculate the insertion loss:

{'out1': [(x_min1, x_max1), (x_min2, x_max2)], 'out2': [(x_min3, x_max3)]}

Returns:

dict

A matrix in the form of a dictionary that maps two output ports to their corresponding crosstalk. crosstalk_matrix[‘out1’][‘out3’] is the crosstalk in ‘out1’ caused by ‘out3’. Because ‘out1’ has two passbands, the crosstalk also has two values. For example:

{'out1': {'out3': array([-83.03362741, -83.59168257]),
          'out4': array([-87.7717227 , -89.87154196]),
          'out5': array([-90.2991268 , -91.66997018]),
          'out6': array([-87.09402634, -87.20215863]),
          'out7': array([-83.25175028, -83.27741616])},
 ...}

near_crosstalk_matrix(bands)

Calculate the crosstalk for every passband in every channel, taking only nearest neighbours into account.

Parameters:

bands

Lower and upper bounds of x for the channels. Can be specified manually or calculated by means of SpectrumAnalyzer.cutoff_passbands() or SpectrumAnalyzer.width_passbands().

Example where channel ‘out1’ has two passbands and ‘out2’ one passband over which we want to calculate the insertion loss:

{'out1': [(x_min1, x_max1), (x_min2, x_max2)], 'out2': [(x_min3, x_max3)]}

Returns:

dict

A matrix in the form of a dictionary that maps two output ports to their corresponding crosstalk. crosstalk_matrix[‘out1’][‘out2’] is the crosstalk in ‘out1’ caused by ‘out2’. Because ‘out1’ has two passbands, the crosstalk also has two values. For example:

{'out1': {'out2': array([-70.78072315, -74.29802755]),
          'out8': array([-72.40049707, -75.09575688])},
 ...}

crosstalk_matrix(bands)

Calculate the crosstalk for every passband in every channel.

Parameters:

bands

Lower and upper bounds of x for the channels. Can be specified manually or calculated by means of SpectrumAnalyzer.cutoff_passbands() or SpectrumAnalyzer.width_passbands().

Example where channel ‘out1’ has two passbands and ‘out2’ one passband over which we want to calculate the insertion loss:

{'out1': [(x_min1, x_max1), (x_min2, x_max2)], 'out2': [(x_min3, x_max3)]}

Returns:

dict

A matrix in the form of a dictionary that maps two output ports to their corresponding crosstalk.

crosstalk_matrix[‘out1’][‘out3’] is the crosstalk in ‘out1’ caused by ‘out3’.
crosstalk_matrix[‘out1’][‘out1’] is the insertion loss variation (ripple) within the passbands of ‘out1’.

Because ‘out1’ has two passbands, the crosstalk also has two values (one for every passband). For example:

{'out1': {'out1': array([-1.7659547 , -2.14301945]),
          'out2': array([-70.78072315, -74.29802755]),
          'out3': array([-83.03362741, -83.59168257]),
          'out4': array([-87.7717227 , -89.87154196]),
          'out5': array([-90.2991268 , -91.66997018]),
          'out6': array([-87.09402634, -87.20215863]),
          'out7': array([-83.25175028, -83.27741616]),
          'out8': array([-72.40049707, -75.09575688])},
...}