pyDynaMapp.utils package

Submodules

pyDynaMapp.utils.math module

pyDynaMapp.utils.math.MAE(array: numpy.ndarray, window_size: int) → numpy.ndarray

Implement a simple moving average estimator.

Args:

array (np.ndarray): Array of values at each timestep. window_size (int): Number of previous values to consider.

Returns:

np.ndarray: expected values.

pyDynaMapp.utils.math.RMSE(array1: numpy.ndarray, array2: numpy.ndarray = None, axis=0) → numpy.ndarray

Compute the RMSE between 2 arrays across all samples.

Args:

array1 (Nsamples, ndof). array2 (Nsamples, ndof).

Returns:

np.ndarray: (Nsamples, ndof)

pyDynaMapp.utils.math.computeCorrelation(array: numpy.ndarray) → numpy.ndarray

Compute the correlation factor between the columns of a numpy array.

Args:

array (Nsamples, ndof).

Returns:

np.ndarray: (ndof, ndof).

pyDynaMapp.utils.math.computeCumulativeCorrelation(array: numpy.ndarray) → numpy.ndarray

Compute the correlation matrix. Args:

• array : (Nsamples, ndof).

Returns:

• numpy-ndarry: (Nsamples,ndof, ndof)

pyDynaMapp.utils.math.conditionNumber(M, threshold=1e-05)

Computes the condition number of a matrix with a check for SVD convergence. Args:

• M : The input matrix.

• threshold : The condition number threshold to check against.

pyDynaMapp.utils.math.cumulativeRMSE(array1: numpy.ndarray, array2: numpy.ndarray) → numpy.ndarray

Compute the RMSE between columns of two arrays, considering the error committed for each joint at the previous time step.

Args:

array1 (Nsamples, ndof). array2 (Nsamples, ndof).

Returns:

np.ndarray: (Nsamples, ndof)

pyDynaMapp.utils.math.discreteTimeIntegral(vector, time_step)

Compute the discrete-time integral of a sampled vector with a given time step.

Args:

vector (array-like): 1-D input vector. time_step (float) : Sampling frequency with which this vector was recorded.

Returns:

int_vector (numpy.ndarray): 1-D integrated vector.

pyDynaMapp.utils.robot_data module

class pyDynaMapp.utils.robot_data.RobotData(data_file_path, ndof=7, time_step=0.001, intIndex=None, stepIndex=1, fnlIndex=None)

Bases: object

Initialize the RobotData object by loading and processing data from a CSV file.

Args:

• data_file_path (str): Path to the CSV data file.

• ndof (int) : number of freedom degree of the manipulator.

• time_step (float) : step time beteewn tow conscutive sample of data

• intIndex (int): Initial index for data slicing.

• stepIndex (int): Step index for periodic data selection.

• fnlIndex (int): Final index for data slicing.

computePositionNoise(method='std') → tuple

Estimates the Noise level in torque sensor mesurements. The noise model is assumed to be a white gaussian noise.

computeTorqueNoise(variable='torque', method='std') → float

Estimates the Noise level in torque sensor mesurements. The noise model is assumed to be a white gaussian noise.

lowPassfilter(cutoff_frequency)

Filtring robot data using a butter low pass filter

updateSamplingRate(new_sampling_rate)

visualizeAcceleration() → None

Plot the joints desired accleration

visualizeCorrelation(variable='torque')

Plot the correlation graph between joints variable data given by variable parameter

visualizeCurrent() → None

Plot the joints current

visualizePosition() → None

Plot the joints position recorded by the sensors.

visualizeTorque() → None

Plot the joints torque

visualizeVelocity() → None

Plot the joints velocity recorded by the sensors.

pyDynaMapp.utils.solver module

pyDynaMapp.utils.solver.luenbergerObserver(A, B, C, desired_poles)

Computes the Luenberger Observer gain matrix L.

Args::

A (np.ndarray): System matrix. B (np.ndarray): Input matrix. C (np.ndarray): Output matrix. desired_poles (list): Desired poles for the observer.

Returns:

L (np.ndarray): Observer gain matrix.

pyDynaMapp.utils.solver.solveAlgebraicRiccatiEquation(A, B, Q, R)

Solve the discrete time algebric riccati equation given by :

Args:

• A, B : System

Returns:

• P ARE solution.

pyDynaMapp.utils.solver.solve_discrete_state_depend_are(A, B, Q, R)

Solve the discrete state depend Riccati equation Ref:

pyDynaMapp.utils.tools module

pyDynaMapp.utils.tools.checkSkewSymmetric(matrix)

Check if the input matrix is skew-symmetric.

pyDynaMapp.utils.tools.clampArray(array: numpy.ndarray, lower_bound, upper_bound)

pyDynaMapp.utils.tools.columnVector(vec)

Convert a vector to a column vector.

pyDynaMapp.utils.tools.deg2rad(angle_degrees)

Converts an angle given in degrees to radians.

pyDynaMapp.utils.tools.matrix2Text(matrix, filename)

Write the values of a matrix to a text file.

pyDynaMapp.utils.tools.plot2Arrays(array1: numpy.ndarray, array2: numpy.ndarray, legend1=None, legend2=None, title=None, color1='red', color2='blue') → None

Given two (n * m) data arrays where n >> m, plot each column data from both arrays in separate subplots.

pyDynaMapp.utils.tools.plot3Arrays(array1: numpy.ndarray, array2: numpy.ndarray, array3: numpy.ndarray, legend1=None, legend2=None, legend3=None, title=None, color1='red', color2='blue', color3='green') → None

Given three (n * m) data arrays where n >> m, plot each column data from all arrays in separate subplots.

pyDynaMapp.utils.tools.plotArray(array: numpy.ndarray, title=None, ylabel=None) → None

Given an ( n * m ) data array where n >> m, plot each coloum data in sperate subplots .

Args:

• array: numpy ndarray

pyDynaMapp.utils.tools.plotElementWiseArray(array: numpy.ndarray, title=None, xlabel=None, ylabel=None)

pyDynaMapp.utils.tools.rad2deg(angle_radians)

Converts an angle given in radians to degrees.

pyDynaMapp.utils.tools.scaleArray(array: numpy.ndarray, lower_bound, upper_bound)

pyDynaMapp.utils.tools.smooth_columns(data: numpy.ndarray, window_size: int = 5) → numpy.ndarray

Smooth each column of the input data matrix using a moving average.

Parameters: - data: np.ndarray, the input data matrix of size N x 7 - window_size: int, the window size for the moving average

Returns: - smoothed_data: np.ndarray, the smoothed data matrix of the same size as input

pyDynaMapp.utils.tools.struct2json(structData, filename)

Saves a Python dictionary to a JSON file.

pyDynaMapp.utils.tools.wrap2deg(angle)

Wrap angle to the interval [-180, 180].

pyDynaMapp.utils.tools.wrapArray(array: numpy.ndarray, lower_bound, upper_bound)

pyDynaMapp.utils.tools.wrapXpi(angle, X)

Wrap angle to the interval [-X*pi, X*pi].

pyDynaMapp.utils.tools.yaml2dict(yamlFilePath) → dict

Get parameters from the config YAML file and return them as a dictionary.

Args:

yamlFilePath (str): Path to the YAML file.

Module contents