Flow#

Measure response to a constant driving velocity.

Functions

EnsemblePack([cli_args])

Collect different runs.

Generate([cli_args])

Generate IO files.

Plot([cli_args])

Basic plot.

Run([cli_args])

Run simulation.

ensemble_average(file, steadystate)

Ensemble average.

interpret_filename(filename)

Split filename in useful information.

Details

depinning_inertia_2024.Flow.EnsemblePack(cli_args=None)#

Collect different runs. Each run corresponds to depinning_inertia_2024.Flow.Run(). After this, the run-files can be deleted (only destroys the possibility to continue the run).

depinning_inertia_2024.Flow.Generate(cli_args=None)#

Generate IO files. A useful command to create jobs is:

shelephant_dump --fmt "Flow_Run --nstep 100000 {}" */id*h5 -o commands_run.yaml
depinning_inertia_2024.Flow.Plot(cli_args=None)#

Basic plot.

depinning_inertia_2024.Flow.Run(cli_args=None)#

Run simulation.

depinning_inertia_2024.Flow.ensemble_average(file: h5py._hl.files.File | dict, steadystate: dict | pathlib.Path)#

Ensemble average. The input file has to be written by depinning_inertia_2024.Flow.EnsemblePack().

Parameters

  • file – Ensemble info (opened HDF5 archive).

  • steadystate –

    Dictionary with u_frame from which the steady state is reached. E.g.:

    steadystate = {
    "v_frame=0,4_id=0000.h5": None,
    "v_frame=0,5_id=0000.h5": 0.5,
}

    If the input is file, it should be a YAML file with the same structure:

    v_frame=0,4_id=0000.h5: null
v_frame=0,5_id=0000.h5: 0.5

Returns

  • v_frame: array with the applied velocities.

  • mean: dictionary with the steady-state means.

  • std: dictionary with the steady-state standard deviations.

  • steadystate: dictionary with the steady-state values.

  • data: dictionary with the steady-state data.

depinning_inertia_2024.Flow.interpret_filename(filename: str) → dict#

Split filename in useful information.