EyeDiagram

class ipkiss3.all.EyeDiagram

Object representing a signal eyediagram

Parameters:

data_stream: array_like

input 1-D data samples

baud_rate: float

symbols per second of input signal

time_step: float

time between two adjacent samples(unit:second)

offset: float

the transformation proportion of the analysis window. Defaults to 0. the value should be within [0,1]

resampling_rate: float

resampling rate for processing original data stream. Defaults to 1. if resampling_rate is 2, the original data will be upsampled. The size of data will be doubled. if resampling_rate is 0.5, the original data will be downsampled. The size of data will be reduced to half.

n_eyes: int

number of complete eyes shown in the figure. Defaults to 1

visualize(show=True, title=None, x_grids=None, y_grids=200, extend_y=0.05, figure=None)

Visualize the signal eyediagram.

Parameters:

show: bool

title: str or None

title of the figure

x_grids: int or None

Number of grids for x-axis of visualization

y_grids: int

Number of grids for y-axis of visualization. Defaults to 200.

extend_y: float

extend the y-axis of the eyediagram figure the value should be within [0,1]. Defaults to 0.05.

Notes

x_grids and y_grids will only affect visualization, which means original data is not processed by setting these two parameters. Larger grids will lead to more scattered distribution of data in the final figure

Examples

import numpy as np
from scipy.signal import ellip, lfilter
from ipkiss3 import all as i3

# Copyright (c) 2015, Warren Weckesser.  All rights reserved.
def demo_data(num_symbols, samples_per_symbol):
    # A random stream of "symbols" (i.e. bits)
    bits = np.random.randint(0, 2, size=num_symbols)

    # Upsample the bit stream.
    sig = np.repeat(bits, samples_per_symbol)

    # Convert the edges of the symbols to ramps.
    r = min(5, samples_per_symbol // 2)
    sig = np.convolve(sig, [1.0 / r] * r, mode="same")

    # Add some noise and pass the signal through a lowpass filter.
    b, a = ellip(4, 0.087, 30, 0.15)
    y = lfilter(b, a, sig + 0.075 * np.random.randn(len(sig)))

    return y

num_symbols = 1000
samples_per_symbol = 20
y = demo_data(num_symbols, samples_per_symbol)

baud_rate = 10e9
time_step = 1.0 / (baud_rate * samples_per_symbol)
eye = i3.EyeDiagram(y, baud_rate=baud_rate, time_step=time_step, resampling_rate=2, offset=0.2, n_eyes=2)
eye.visualize(extend_y=0.2)