PyDynamic
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Getting started:

  • Python library for the analysis of dynamic measurements
    • Archived
    • Original description
    • Table of content
    • Quickstart
    • Features
    • Module diagram
    • Documentation
    • Installation
    • Contributing
    • Examples
    • Citation
    • Acknowledgement
    • Disclaimer
    • License

Detailed information:

  • Installation
    • Quick setup (not recommended)
      • Updating to the newest version
    • Proper Python setup with virtual environment (recommended)
      • Set up a virtual environment
        • Create a venv Python environment on Windows
        • Create a venv Python environment on Mac or Linux
        • Create an Anaconda Python environment
      • Install PyDynamic via pip
      • Optional Jupyter Notebook dependencies
      • Install known to work dependencies’ versions
  • Changelog
    • v2.5.1 (2024-05-31)
      • Fix
      • Documentation
    • v2.5.0 (2024-04-18)
      • Feature
      • Documentation
    • v2.4.2 (2023-08-11)
      • Fix
      • Documentation
    • v2.4.1 (2023-07-27)
      • Fix
    • v2.4.0 (2023-03-20)
      • Feature
      • Fix
    • v2.3.2 (2022-12-20)
      • Fix
      • Documentation
    • v2.3.1 (2022-11-08)
      • Fix
      • Documentation
    • v2.3.0 (2022-08-18)
      • Feature
      • Fix
    • v2.2.0 (2022-04-22)
      • Feature
    • v2.1.3 (2022-04-19)
      • Fix
    • v2.1.2 (2022-02-07)
      • Fix
    • v2.1.1 (2021-12-18)
      • Fix
      • Documentation
    • v2.1.0 (2021-12-03)
      • Feature
      • Documentation
    • v2.0.0 (2021-11-05)
      • Feature
      • Fix
      • Breaking
      • Documentation
    • v1.11.1 (2021-10-20)
      • Fix
    • v1.11.0 (2021-10-15)
      • Feature
      • Fix
      • Documentation
    • v1.10.0 (2021-09-28)
      • Feature
      • Fix
    • v1.9.2 (2021-09-21)
      • Fix
    • v1.9.1 (2021-09-15)
      • Fix
      • Documentation
    • v1.9.0 (2021-05-11)
      • Feature
      • Documentation
    • v1.8.0 (2021-04-28)
      • Feature
      • Documentation
    • v1.7.0 (2021-02-16)
      • Feature
      • Documentation
    • v1.6.1 (2020-10-29)
      • Fix
  • Contributor Covenant Code of Conduct
    • Our Pledge
    • Our Standards
    • Enforcement Responsibilities
    • Scope
    • Enforcement
    • Enforcement Guidelines
      • 1. Correction
      • 2. Warning
      • 3. Temporary Ban
      • 4. Permanent Ban
    • Attribution
  • Advices and tips for contributors
    • Guiding principles
    • Get started developing
      • Get the code on GitHub and locally
      • Initial development setup
      • Advised toolset
      • Coding style
      • Commit messages
        • Commit message structure
        • Commit message styling
        • BREAKING CHANGEs
        • Examples
      • Testing
    • Workflow for adding completely new functionality
    • Documentation
      • User documentation
      • Examples
      • Comments in the code
    • Manage dependencies
    • Licensing

Examples:

  • Examples
    • Quick Examples
    • Detailed examples
      • Design of a digital deconvolution filter (FIR type)
        • Problem description
      • Uncertainty propagation for IIR filters
        • Linearisation-based uncertainty propagation
        • Implementation in PyDynamic
        • Example
        • Monte-Carlo method for uncertainty propagation
        • Basic workflow in PyDynamic
      • Deconvolution in the frequency domain (DFT)
        • Propagation from time to frequency domain
        • Uncertainties for measurement system w.r.t. real and imaginary parts
        • Deconvolution in the frequency domain
        • Propagation from frequency to time domain
        • Summary of PyDynamic workflow for deconvolution in DFT domain
      • DFT and inverse DFT with PyDynamic - best practice guide
        • Prerequisites
        • 1) Discrete Fourier Transform (DFT)
        • 2) Inverse Discrete Fourier Transform (iDFT)
        • 3) Multiply Spectra in the Frequency Domain
        • 4) Deconvolve Signals by Division of Spectra
        • 5) Exemplary Regularization
      • Input estimation for shock acceleration
      • Design of a digital deconvolution filter (FIR type)

Tutorials:

  • Get assistance in using PyDynamic
    • Getting started with the tutorials
    • Deconvolution
    • Uncertainty

Code Reference:

  • Evaluation of uncertainties
    • Uncertainty evaluation for convolutions
      • convolve_unc()
    • Uncertainty evaluation for the DFT
      • AmpPhase2DFT()
      • AmpPhase2Time()
      • DFT2AmpPhase()
      • DFT_deconv()
      • DFT_multiply()
      • DFT_transferfunction()
      • GUM_DFT()
      • GUM_DFTfreq()
      • GUM_iDFT()
      • Time2AmpPhase()
      • Time2AmpPhase_multi()
    • Uncertainty evaluation for the DWT
      • dwt()
      • dwt_max_level()
      • filter_design()
      • inv_dwt()
      • wave_dec()
      • wave_dec_realtime()
      • wave_rec()
    • Uncertainty evaluation for digital filtering
      • FIRuncFilter()
      • IIR_get_initial_state()
      • IIRuncFilter()
    • Monte Carlo methods for digital filtering
      • MC()
      • SMC()
      • UMC()
      • UMC_generic()
    • Uncertainty evaluation for interpolation
      • interp1d_unc()
      • make_equidistant()
    • Uncertainty evaluation for multiplication
      • hadamar_product()
      • window_application()
  • Model estimation
    • Fitting filters to frequency response or reciprocal
      • LSFIR()
      • LSIIR()
    • Identification of transfer function models
      • fit_som()
  • Miscellaneous
    • Tools for 2nd order systems
      • sos_FreqResp()
      • sos_absphase()
      • sos_phys2filter()
      • sos_realimag()
    • Tools for digital filters
      • db()
      • grpdelay()
      • isstable()
      • kaiser_lowpass()
      • mapinside()
      • savitzky_golay()
      • ua()
    • Test signals
      • GaussianPulse()
      • corr_noise
      • multi_sine()
      • rect()
      • shocklikeGaussian()
      • sine()
      • squarepulse()
    • Noise related functions
      • ARMA()
      • get_alpha()
      • power_law_acf()
      • power_law_noise()
      • white_gaussian()
    • Miscellaneous useful helper functions
      • FreqResp2RealImag()
      • complex_2_real_imag()
      • is_2d_matrix()
      • is_2d_square_matrix()
      • is_vector()
      • make_equidistant()
      • make_semiposdef()
      • normalize_vector_or_matrix()
      • number_of_rows_equals_vector_dim()
      • plot_vectors_and_covariances_comparison()
      • print_mat()
      • print_vec()
      • progress_bar()
      • real_imag_2_complex()
      • separate_real_imag_of_mc_samples()
      • separate_real_imag_of_vector()
      • shift_uncertainty()
      • trimOrPad()
  • Signal
    • Signal
      • Signal.Fs
      • Signal.Ts
      • Signal.apply_filter()
      • Signal.name
      • Signal.standard_uncertainties
      • Signal.time
      • Signal.uncertainty
      • Signal.unit_time
      • Signal.unit_values
      • Signal.values
PyDynamic
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© Copyright 2022, S. Eichstädt (PTB), M. Gruber (PTB), B. Ludwig (PTB), T. Bruns (PTB), I. Smith (NPL). Revision 8a25ec06.

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