Programmatically generate synthetic sequence data such as time series, strings, DNA, etc. Sequence data generation is fully controlled by the user. sequgen does not build models from real-world sequence data.

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Unit tests

pip3 install sequgen

This usage example involves generating time series data. We generate a time series with three channels: 1. a normal distribution, 2. Gaussian noise, and 3. the combination (sum) of the first two channels. The normal distribution is positioned between 8 and 12 on an abstract time axis of 100 intervals starting at 0 and ending at 20. The standard deviation of the distribution is a value between 1 and 2 and its peak has a height between 4 and 5. For the Gaussian noise we use the default values (standard deviation 1 and average value 0). The third channel is defined as the sum of the other two channels. After creating the three channels, graphs with their values are plotted:

from matplotlib import pyplot as plt
import numpy
from sequgen.deterministic.normal_peak import normal_peak
from sequgen.stochastic.gaussian import gaussian
from sequgen.parameter_space import ParameterSpace
from sequgen.dimension import Dimension

time_axis = numpy.linspace(start=0, stop=20, num=101)
parameter_space_0 = ParameterSpace([
    Dimension("location", 8, 12),
    Dimension("stddev", 1, 2),
    Dimension("height", 4, 5),
])

channel_1 = normal_peak(time_axis, **parameter_space_0.sample())
channel_2 = gaussian(time_axis)
channel_3 = channel_1 + channel_2
channels = { "channel 1: normal peak": channel_1,
             "channel 2: gaussian noise": channel_2,
             "channel 3: combined": channel_3 }

i = 0
for title, channel in channels.items():
    plt.subplot(len(channels), 1, i+1)
    plt.plot(time_axis, channel)
    plt.title(title, y=0.75, x=0.01, loc="left")
    i += 1
plt.show()

And these are the results:

You can find more usage examples in the notebooks repository on GitHub: https://github.com/sequgen/notebooks.

For developer documentation, go to the developer's README.

If you want to contribute to the development of sequgen, have a look at the contribution guidelines.

This package was created with Cookiecutter and the NLeSC/python-template.