Time series & comparisons

Where the accessors come from

The examples on this page 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).

Time series with a growth-rate fit

energy.plasma.plot.timeseries(logy=True, fit=(0.0, 2.0))

Overlays an exponential fit on a window of the series and reports the rate in result.fit_results. See Diagnostics for the damping-rate variant (fit the envelope of an oscillating signal instead).

The data behind this plot: energy.plasma.data.timeseries(other_run_energy) returns the validated list of series; the fit is .plasma.analysis.growth_rate() (see Selecting data).

The same plot with Plotly

The same call with backend="plotly": an interactive figure with the same data, fits and labels (see Interactive plots with Plotly).

energy.plasma.plot.timeseries(logy=True, fit=(0.0, 2.0), backend="plotly")

Loading interactive chart…

Several series in one axes

energy.plasma.plot.timeseries(other_run_energy, logy=True)

Passing further arrays plots them together; if they come from different runs (different attrs["run_name"]), each line is labeled by its run.

Several plots in one figure

plasma_plots.figure(nrows, ncols) makes a figure of panels; every plot method that takes ax= draws into one of them. Here the growing energy with its fit above, and the relative drift of the total energy below:

import plasma_plots

with plasma_plots.figure(2, 1, sharex=True) as fig:
    energy.plasma.plot.timeseries(fit=(0.0, 2.0), ax=fig[0])
    total.plasma.analysis.drift().plasma.plot.timeseries(logy=False, ax=fig[1])
fig.save("energies.png")
fig.results[0].fit_results[0].rate  # each panel's result, with its fits

Panels keep their own colorbars and legends; sharex/sharey share the axes (and, in Plotly, their zoom), and title= goes above all panels. Panels left empty are hidden.

The same plot with Plotly

backend="plotly" finishes the figure as one interactive Plotly figure (see Interactive plots with Plotly); the plots inside the block draw into their panels as before:

with plasma_plots.figure(2, 1, sharex=True, backend="plotly") as fig:
    energy.plasma.plot.timeseries(fit=(0.0, 2.0), ax=fig[0])
    total.plasma.analysis.drift().plasma.plot.timeseries(logy=False, ax=fig[1])
fig.save("energies.html")

Loading interactive chart…

Exact and expected curves

reference draws exact or expected curves over the series, dashed and black: a function of t, an array on the same times, a (t, values) pair, or a dict of labels to these. A label starting with _ stays out of the legend. For a damped Langmuir wave, whose k = 1 amplitude comes from project_mode, against the exact envelope:

amplitude = e1.plasma.analysis.project_mode(dim="eta1", number=1)
amplitude.plasma.plot.timeseries(
    logy=False,
    reference={
        "±0.1 e^(-γt)": lambda t: 0.1 * np.exp(-gamma * t),
        "_lower": lambda t: -0.1 * np.exp(-gamma * t),
    },
)

Measured against theory

against_theory plots measured values over a parameter against a theory curve, with their relative error in a second panel. The measured values can be a 1-D array over the parameter, such as the frequencies from trace_branch over k or growth rates over mode numbers. They can also be an (x, y) pair, or a dict of several runs or methods. The theory can be one function or a dict of several:

traced = spectrum.plasma.analysis.trace_branch(bohm_gross, k_range=(1.5, 5.5))
traced.omega.plasma.plot.against_theory(
    {"Bohm-Gross": bohm_gross, "cold plasma": lambda k: 1 + 0 * k}
)

from plasma_plots.plotting import plot_measured_vs_theory

plot_measured_vs_theory(
    {"run A": rates_a, "run B": (m, rates_b)},
    theory,
    logx=True,
    show_error=False,
)

The error panel compares every measured series with the first theory function.

Comparing two runs directly

field.plasma.plot.compare(reference_field, mode="difference")
field.plasma.plot.compare(reference_field, mode="ratio")

Aligns the two arrays and plots their difference or ratio as a 1-D lineout — useful for a convergence study or comparing a run against a reference.

The data behind this plot: field.plasma.data.compare(reference_field, mode="ratio") returns the aligned difference or ratio as a 1-D array (see Selecting data).

The same plot with Plotly

The same call with backend="plotly": an interactive figure with the same data, fits and labels (see Interactive plots with Plotly).

field.plasma.plot.compare(reference_field, mode="ratio", backend="plotly")

Loading interactive chart…