2.1.2.6. Noise Models

2.1.2.6.1. Main Functions

teaspoon.MakeData.DynSysLib.noise_models.exponential_noise(sigma=1.0, L=1000, fs=1, SampleSize=1000)[source]

Generate a noise signal sampled from an exponential distribution.

../../../_images/exponential_noise.png
Parameters:

  • sigma (Optional[float]) – Exponential distribution scale.

  • L (Optional[int]) – amount of time to solve simulation for.

  • fs (Optional[int]) – sampling rate for simulation.

  • SampleSize (Optional[int]) – length of sample at end of entire time series

Returns:

Array of the time indices as t and the simulation time series ts

Return type:

array

teaspoon.MakeData.DynSysLib.noise_models.gaussian_noise(sigma=1.0, mu=0.0, L=1000, fs=1, SampleSize=1000)[source]

Generate a noise signal sampled from a Gaussian distribution.

../../../_images/gaussian_noise.png
Parameters:

  • sigma (Optional[float]) – Standard deviation of the normal distribution.

  • mu (Optional[float]) – Mean of the normal distribution.

  • L (Optional[int]) – amount of time to solve simulation for.

  • fs (Optional[int]) – sampling rate for simulation.

  • SampleSize (Optional[int]) – length of sample at end of entire time series

Returns:

Array of the time indices as t and the simulation time series ts

Return type:

array

teaspoon.MakeData.DynSysLib.noise_models.rayleigh_noise(sigma=1.0, L=1000, fs=1, SampleSize=1000)[source]

Generate a noise signal sampled from a Rayleigh distribution.

../../../_images/rayleigh_noise.png
Parameters:

  • sigma (Optional[float]) – Rayleigh distribution mode.

  • L (Optional[int]) – amount of time to solve simulation for.

  • fs (Optional[int]) – sampling rate for simulation.

  • SampleSize (Optional[int]) – length of sample at end of entire time series

Returns:

Array of the time indices as t and the simulation time series ts

Return type:

array

teaspoon.MakeData.DynSysLib.noise_models.uniform_noise(a=-1.0, b=1.0, L=1000, fs=1, SampleSize=1000)[source]

Generate a noise signal sampled from a uniform distribution.

../../../_images/uniform_noise.png
Parameters:

  • a (Optional[float]) – Uniform distribution lower bound.

  • b (Optional[float]) – Uniform distribution upper bound.

  • L (Optional[int]) – amount of time to solve simulation for.

  • fs (Optional[int]) – sampling rate for simulation.

  • SampleSize (Optional[int]) – length of sample at end of entire time series

Returns:

Array of the time indices as t and the simulation time series ts

Return type:

array

2.1.2.6.2. Meta Function

This function is for being able to input into the original teaspoon.MakeData.DynSysLib.DynamicSystems function with default parameters.

teaspoon.MakeData.DynSysLib.noise_models.noise_models(system, dynamic_state=None, L=None, fs=None, SampleSize=None, parameters=None, InitialConditions=None)[source]