Functions

src.PythonDSSAnalysis.DSSHandler.data_normalizer(data, inputs=0)

Normalizes Data to Users Input, Creates conserved quantity for DSS analysis

Parameters:

  • data (numpy.ndarray) – Data set to be normalized

  • inputs (dictionary) – Input file with corresponding model and data settings

Returns:

Betas – Array with normalized data (array of Beta’s in DSS terminology)

Return type:

numpy.ndarray

src.PythonDSSAnalysis.DSSHandler.effect_parameter_solver(effectmetric)

Returns the effect parameter. EQ 20

Parameters:

effectmetric (numpy.ndarray) – Array with effect metric values

Returns:

effectparameter – Float value of effect parameter.

Return type:

float

src.PythonDSSAnalysis.DSSHandler.geodesic_separation(dataBeta, dataD, modelEffectMetric, dataEffectMetric)

Returns the geodesic separation at each process time step. Eq 45 in DSS bubble applications

Parameters:

  • dataBeta (numpy.ndarray) – Array with normalized conserved quantity of interest of the experiment

  • modelEffectMetric (numpy.ndarray) – Array with model’s effect metric values

  • dataEffectMetric (numpy.ndarray) – Array with experiment’s effect metric values

  • dataD (numpy.ndarray) – Array with temporal displacement rate values of the experiment

Returns:

  • Local separation (numpy.ndarray) – Array with geodesic separation values at each process timestep between model and prototype.

  • Total separation (float) – float value of total geodesic separation

src.PythonDSSAnalysis.DSSHandler.model_data_generator(time, inputs)

Generates the model dataset for comparison to experimental data

Parameters:

  • data (inputs) – Input file with model values specified

  • time (numpy.ndarray) – Reference time value of datasets

Returns:

Array with non-normalized data generated from model

Return type:

ModelData

src.PythonDSSAnalysis.DSSHandler.normalized_coordinates(omega, referencetime, processtime, processaction)

Returns the normalized coordinates and parameters to assess scale distortion. EQ 14a-d

Parameters:

  • omega (numpy.ndarray) – Array with omega values

  • referencetime (numpy.ndarray) – Array with reference time values

  • processtime (numpy.ndarray) – Array with process time values

  • processaction (float) – Float value of process action

Returns:

  • effectmetric (numpy.ndarray) – Array of effect metric values

  • normalizedreferencetime (numpy.ndarray) – Array of normalized reference time values

  • normalized reference time (numpy.ndarray) – Array of normalzied process time values

src.PythonDSSAnalysis.DSSHandler.process_action_solver(time, temporalDisplacement)

Finds the process action (tau_s) using numerical integration. Eq 12

Parameters:

  • time (numpy.ndarray) – Array with reference time values

  • temporalDisplacement (numpy.ndarray) – Array with Temporal Displacement Values (D)

Returns:

process action – Value of Process time

Return type:

float

src.PythonDSSAnalysis.DSSHandler.process_time_generator(beta, omega)

Generates process time from an input of beta and omega values

Parameters:

  • beta (numpy.ndarray) – Array with normalized conserved quantity of interest (Betas)

  • omega (numpy.ndarray) – Array with normalized agents of change (Omegas)

Returns:

Tau – Array with process time values

Return type:

numpy.ndarray

src.PythonDSSAnalysis.DSSHandler.standard_error(localseparation)

Returns an estimate of the standard error. 95% of values fall within +- 2sigma_est values. EQ 47 in DSS Bubble Dynamics Applications.

Parameters:

localseparation (numpy.ndarray) – Array with normalized conserved quantity of interest of the data local separation between model and prototype at each process time point

Returns:

sigmaest – Float value of estimate of standard error (total distortion)

Return type:

float

src.PythonDSSAnalysis.DSSHandler.temporal_displacement_generator(beta, omega, omegaprimes)

Generates the Temporal Displacement value.

Parameters:

  • beta (numpy.ndarray) – Array with normalized conserved quantity of interest (Betas)

  • omega (numpy.ndarray) – Array with normalized agents of change (Omegas)

  • omegaprimes (numpy.ndarray) – Array with time derivative of Omegas

Returns:

D – Array with Temporal Displacement Values

Return type:

numpy.ndarray

src.PythonDSSAnalysis.DSSHandler.time_derivative(data, time)

Gets timed derivative using finite differences (can calculate omega or omega prime, first and second time derivative of beta respectively)

Parameters:
Returns:

timederivative – Array with normalized data (array of omegas’s in DSS terminology)

Return type:

numpy.ndarray

src.PythonDSSAnalysis.DSSHandler.value_to_array(size, value)

Returns an array with the first value being the value of interest while the rest are numpy.nan data type, this is for data saving purposes

Parameters:

  • size (int) – Int value with size of array

  • value (float) – float value of interest

Returns:

productArray – Array with value of interest in first element while rest is numpy.nan

Return type:

numpy.ndarray