out.plot and out.analysis

Plots and diagnostics of a whole run, on a Struphy Output object. out.plot() on its own draws the scalar overview.

Where the accessors come from

The examples use output of a Struphy Output, which loads plasma-plots and its .plasma accessors. For other xarray data, run import plasma_plots first (see Getting started).

out.plot

Plots of a whole run, as out.plot.<kind>(...), constructed as OutputPlots(out).

scalars

scalarsmethod#

View source

def scalars(names=None, *, relative_to: str | None = None, logy: bool = False, backend: Backend | None = None)

Overview of the scalar time series in one axes.

Parameters

Name	Type	Default	Description
names	list of str	None	Scalars to show; all by default.
relative_to	str	None	Show every scalar divided by this one.
logy	bool	False	Logarithmic value axis.
backend	('matplotlib', 'plotly', 'tikz')	"matplotlib"	Draw with Matplotlib, as an interactive Plotly figure, or as TikZ/pgfplots code for LaTeX (in result.fig; needs plotly or maxplotlib, see plasma_plots.plotly_backend and plasma_plots.tikz_backend). Default: the one set with plasma_plots.set_backend(), "matplotlib" unless changed.

Returns

PlotResult

The figure, the axes and the drawn lines, titled with the run’s label.

See Also

plasma_plots.plotting.plot_scalars() : The function behind this method.

Examples

>>> out.plot.scalars()
>>> out.plot.scalars(["en_U", "en_B"], logy=True)

energies

energiesmethod#

View source

def energies(parts=None, total: str | None = 'en_tot', groups: dict | None = None, logy: bool = False, backend: Backend | None = None)

Plot the run’s energy budget from its energy scalars (en_* or *_energy).

Shows the energy scalars, the relative drift of total, and, with groups (e.g. {"wave": ["en_U", "en_B", "en_p"], "energetic ions": ["en_fv", "en_fB"]}), the energy exchanged between them.

Parameters

Name	Type	Default	Description
parts	sequence of str	None	The energies drawn in the first panel. Default: the scalars [energy_names()][energy_names] finds (en_* and *_energy), except total.
total	str or None	'en_tot'	The total energy; the second panel is left out if it is None or not among the scalars. Default: "en_tot", or "total_energy" for a run that names its energies that way.
groups	dict of str to list of str	None	A label to the names it sums, e.g. {"wave": ["en_U", "en_B", "en_p"], "energetic ions": ["en_fv", "en_fB"]}. Default: no exchange panel.
logy	bool	False	Use a logarithmic value axis in the first panel. Default: False.
backend	('matplotlib', 'plotly', 'tikz')	"matplotlib"	Draw with Matplotlib, as an interactive Plotly figure, or as TikZ/pgfplots code for LaTeX (in result.fig; needs plotly or maxplotlib, see plasma_plots.plotly_backend and plasma_plots.tikz_backend). Default: the one set with plasma_plots.set_backend(), "matplotlib" unless changed.

Returns

PlotResult

The figure, the axes and the drawn lines, titled with the run’s label.

See Also

plasma_plots.plotting.plot_energy_budget() : The function behind this method.

Examples

>>> out.plot.energies()
>>> out.plot.energies(
...     groups={
...         "wave": ["en_U", "en_B", "en_p"],
...         "energetic ions": ["en_fv", "en_fB"],
...     }
... )

equilibrium

equilibriummethod#

View source

def equilibrium(ax=None, *, backend: Backend | None = None)

Plot radial profiles of this run’s fluid equilibrium (out.equil, out.domain).

Parameters

Name	Type	Default	Description
ax	matplotlib.axes.Axes	None	The axes to draw into. Default: a new figure.
backend	('matplotlib', 'plotly', 'tikz')	"matplotlib"	Draw with Matplotlib, as an interactive Plotly figure, or as TikZ/pgfplots code for LaTeX (in result.fig; needs plotly or maxplotlib, see plasma_plots.plotly_backend and plasma_plots.tikz_backend). Default: the one set with plasma_plots.set_backend(), "matplotlib" unless changed.

Returns

PlotResult

The figure, the axes and the drawn lines.

See Also

plasma_plots.plotting.plot_equilibrium_profile() : The function behind this method.

Examples

>>> out.plot.equilibrium()

equilibrium_3d

equilibrium_3dmethod#

View source

def equilibrium_3d(scalars: str = 'p0', cmap='viridis')

Create a PyVista view of this run’s fluid equilibrium; call .show() on the returned plotter.

Parameters

Name	Type	Default	Description
scalars	str	'p0'	One of equil’s profile methods ("p0", "n0", …). Default: "p0".
cmap	str	'viridis'	The colormap. Default: "viridis".

Returns

pyvista.Plotter

The scene, not yet shown.

See Also

plasma_plots.plotting.show_equilibrium() : The function behind this method.

Examples

>>> out.plot.equilibrium_3d(scalars="p0").show()

domain_3d

domain_3dmethod#

View source

def domain_3d(n1: int = 8, n2: int = 32, n3: int = 32, surface: bool = True)

Draw a PyVista wireframe of this run’s mapping (out.domain); call .show() on it.

Parameters

Name	Type	Default	Description
n1	int	8	Grid lines along eta1. Default: 8.
n2	int	32	Grid lines along eta2. Default: 32.
n3	int	32	Grid lines along eta3. Default: 32.
surface	bool	True	Draw the translucent boundary surface. Default: True.

Returns

pyvista.Plotter

The scene, not yet shown.

See Also

plasma_plots.pyvista_plots.pyvista_domain() : The function behind this method.

Examples

>>> out.plot.domain_3d().show()
>>> out.plot.domain_3d(n3=1).show()

profile

profileproperty#

View source

profile: 'ProfilePlots'

Plots of this run’s timing regions, e.g. out.plot.profile.gantt().

Needs the optional scope-profiler extra (pip install "plasma-plots[profiling]") and a run recorded with sim.run(profiling_activated=True).

Returns

ProfilePlots

The profiling plots of this run.

out.plot.profile

Plots of one run’s timing regions (out.profile.results), as out.plot.profile.<kind>(...).

gantt

ganttmethod#

View source

def gantt(return_fig: bool = True, verbose: bool = False, **kwargs)

A timeline of every recorded region, one row per rank.

Parameters

Name	Type	Default	Description
return_fig	bool	True	Return the rendered figure (scope-profiler’s own default is to return None). Default: True.
verbose	bool	False	Let scope-profiler print progress information. Default: False.
**kwargs		{}	Passed on to scope-profiler’s plot_gantt (ranks, include/exclude, backend, filepath, min_duration, …).

Returns

tuple or plotly.graph_objects.Figure or None

Whatever scope-profiler returns: (fig, axes) for the default matplotlib backend, a Plotly figure with backend="plotly", None with return_fig=False.

Examples

>>> out.plot.profile.gantt()

flame

flamemethod#

View source

def flame(return_fig: bool = True, verbose: bool = False, **kwargs)

A flame chart reconstructing the call stack from region timings.

Parameters

Name	Type	Default	Description
return_fig	bool	True	Return the rendered figure (scope-profiler’s own default is to return None). Default: True.
verbose	bool	False	Let scope-profiler print progress information. Default: False.
**kwargs		{}	Passed on to scope-profiler’s plot_flame (ranks, include/exclude, backend, filepath, …).

Returns

tuple or plotly.graph_objects.Figure or None

Whatever scope-profiler returns: (fig, axes) for the default matplotlib backend, a Plotly figure with backend="plotly", None with return_fig=False.

Examples

>>> out.plot.profile.flame()

callgraph

callgraphmethod#

View source

def callgraph(return_fig: bool = True, verbose: bool = False, **kwargs)

The explicit call graph (which region calls which), without timings.

Parameters

Name	Type	Default	Description
return_fig	bool	True	Return the rendered figure (scope-profiler’s own default is to return None). Default: True.
verbose	bool	False	Let scope-profiler print progress information. Default: False.
**kwargs		{}	Passed on to scope-profiler’s plot_callgraph (rank, include/exclude, backend, compact, fluid, …).

Returns

tuple or plotly.graph_objects.Figure or None

Whatever scope-profiler returns: (fig, axes) for the default matplotlib backend, a Plotly figure with backend="plotly", None with return_fig=False.

Examples

>>> out.plot.profile.callgraph()

out.analysis

Spectral diagnostics of a run’s products, as out.analysis.<kind>(product, ...).

fft

fftmethod#

View source

def fft(product, *, dim: str, detrend: bool = False, window: str | None = None)

Compute two-sided Fourier coefficients of a product along dim.

Parameters

Name	Type	Default	Description
product	str or xarray.DataArray	required	A product name (e.g. "mhd/velocity"), evaluated with out.evaluate, or an already selected array.
dim	str	required	The dimension to transform.
detrend	bool	False	Subtract the mean along dim first. Default: False.
window	(None, 'hann')	None	Multiply by a periodic Hann window (see [hann()][hann]) first. Default: None (boxcar).

Returns

xarray.DataArray

Complex coefficients, with dim replaced by omega (time) or k_<dim>.

See Also

plasma_plots.spectral.fft() : The function behind this method.

Examples

>>> out.analysis.fft(phi.isel(t=-1, eta2=0, eta3=0), dim="eta1")

time_fft

time_fftmethod#

View source

def time_fft(product, *, detrend: bool = False, window: str | None = None)

Compute one-sided temporal Fourier coefficients and per-bin power of a product.

Parameters

Name	Type	Default	Description
product	str or xarray.DataArray	required	A product name (e.g. "mhd/velocity"), evaluated with out.evaluate, or an already selected array.
detrend	bool	False	Subtract the temporal mean first. Default: False.
window	(None, 'hann')	None	Multiply by a periodic Hann window (see [hann()][hann]) first. Default: None (boxcar).

Returns

xarray.Dataset

The complex coefficients and the power over omega.

See Also

plasma_plots.spectral.time_fft() : The function behind this method.

Examples

>>> out.analysis.time_fft(phi.isel(eta1=0.5, eta2=0, eta3=0), window="hann")

filter_time

filter_timemethod#

View source

def filter_time(product, *, dims=None, omega_min: float = 1e-08, pad_bins: int = 0)

Reconstruct the dominant temporal frequency band of a product.

Parameters

Name	Type	Default	Description
product	str or xarray.DataArray	required	A product name (e.g. "mhd/velocity"), evaluated with out.evaluate, or an already selected array.
dims	str or sequence of str	None	Non-time dimensions to sum the power over before choosing the band. Default: all dimensions except t and component. Pass dims=() for independent filtering at each point.
omega_min	float	1e-08	Finite, positive lowest frequency considered, which excludes DC. Default: 1e-8.
pad_bins	int	0	Nonnegative number of extra bins on each side of the band. Default: 0.

Returns

TimeFilterResult

The filtered field and the reduced spectrum with the selected band.

See Also

plasma_plots.spectral.filter_time() : The function behind this method.

Examples

>>> result = out.analysis.filter_time(phi)
>>> result.filtered.plasma.plot.slice(t=-1, eta3=0)

linear_mhd_energies

linear_mhd_energiesmethod#

View source

def linear_mhd_energies(velocity='mhd/velocity', b_field='em_fields/b_field', pressure='mhd/pressure', gamma: float = 5 / 3)

Compute LinearMHD’s energy scalars from fields, as a Dataset of time series.

The scalars are en_U, en_B, en_thermal, en_p and en_tot = en_U + en_B + en_thermal. Same definitions as the scalars saved during the run (en_U = 1/2 u^T M2n u, …), with the run’s domain and equilibrium (n0, p0), but evaluated by quadrature on the post-processing grid, so they also work for fields that were never simulated: pass a filtered array (e.g. filter_time(...).filtered) to get the energy in one mode. Each argument is a raw field name (evaluated at the Gauss points of quadrature_grid(), in its FEEC space’s own representation), an array in that representation (on the Gauss grid its weights are exact; elsewhere see quadrature_weights()), or None to skip it: 2-form components for velocity and b_field (out.evaluate("mhd/velocity", representation="2")), a 3-form for pressure (representation="3"). The default post-processing products are in other representations ("norm", "0") and would give wrong energies. Points where p0 vanishes are left out of en_thermal.

Parameters

Name	Type	Default	Description
velocity	(str, xarray.DataArray or None)	'mhd/velocity'	The velocity as 2-form components (en_U, weighted by n0). Default: "mhd/velocity".
b_field	(str, xarray.DataArray or None)	'em_fields/b_field'	The magnetic field as 2-form components (en_B). Default: "em_fields/b_field".
pressure	(str, xarray.DataArray or None)	'mhd/pressure'	The pressure as a 3-form (en_thermal, weighted by 1/p0, and en_p). Default: "mhd/pressure".
gamma	float	5 / 3	The adiabatic index: en_thermal is normalized by 1/gamma and en_p = ∫ p / (gamma - 1). Default: 5/3.

Returns

xarray.Dataset

One time series per computed scalar (only those whose fields were given), plus en_tot.

Raises

ValueError

If velocity, b_field and pressure are all None.

See Also

quadrature_grid() : The Gauss points the raw fields are evaluated at. plasma_plots.analysis.field_energy() : The energy integral of one field.

Examples

>>> energies = out.analysis.linear_mhd_energies()
>>> filtered = out.analysis.filter_time(
...     out.evaluate("mhd/velocity", representation="2")
... ).filtered
>>> out.analysis.linear_mhd_energies(
...     velocity=filtered, b_field=None, pressure=None
... )

quadrature_grid

quadrature_gridmethod#

View source

def quadrature_grid()

Return the run’s Gauss-Legendre quadrature points and weights in each logical direction.

The points (spline degree + 1 per element) and their weights for each of eta1, eta2, eta3 form the grid on which spline fields squared integrate exactly. Evaluate a field there (out.evaluate(name, eta1=etas["eta1"], ..., representation="2")), filter it, and linear_mhd_energies or field_energy(..., quadrature=weights) give its energy as the run’s own scalars would.

Returns

etasdict of str to numpy.ndarray

The points in [0, 1] for "eta1", "eta2", "eta3".

weightsdict of str to numpy.ndarray

The matching quadrature weights, summing to 1 in each direction.

Examples

>>> etas, weights = out.analysis.quadrature_grid()
>>> b = out.evaluate(
...     "em_fields/b_field",
...     eta1=etas["eta1"],
...     eta2=etas["eta2"],
...     eta3=etas["eta3"],
...     representation="2",
... )

mode_spectrum

mode_spectrummethod#

View source

def mode_spectrum(product, *, dims=('eta2', 'eta3'), names=('m', 'n'), periods=1.0)

Compute complex amplitudes of a product over poloidal/toroidal mode numbers.

Parameters

Name	Type	Default	Description
product	str or xarray.DataArray	required	A product name (e.g. "mhd/velocity"), evaluated with out.evaluate, or an already selected array.
dims	str or sequence of str	('eta2', 'eta3')	The periodic dimensions to transform. Default: the two angles of the logical dimensions (see plasma_plots.arrays.logical_dims()), ("eta2", "eta3") for Struphy.
names	str or sequence of str	('m', 'n')	The name of the mode number of each dimension, one per dimension. Default: ("m", "n").
periods	float or sequence of float	1.0	Each direction’s period in its coordinate: one number for all, or one per dimension. Default: the coordinate’s period attribute (see plasma_plots.arrays.angle_period()), else 1.0.

Returns

xarray.DataArray

Complex amplitudes over the mode numbers names and every remaining dimension.

See Also

plasma_plots.spectral.mode_spectrum() : The function behind this method.

Examples

>>> out.analysis.mode_spectrum("em_fields/phi_log")
>>> out.analysis.mode_spectrum(phi.isel(t=-1), dims="eta3", names="n")