opal.visualization package

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opal.visualization.AmrPlotter module

class opal.visualization.AmrPlotter.AmrPlotter[source]

Bases: BasePlotter

__init__()[source]
line_plot(axis, field, **kwargs)[source]

Plot a line plot of 3D data along an axis

Parameters:

  • axis (str) – Take a line cut along this axis (‘x’, ‘y’, ‘z’)

  • field (str) – Quantity of y-axis

  • unit (str, optional) – Unit of y-axis

  • center ((float, float)) – Center of plot through which line should go

particle_phase_space_plot(axis, **kwargs)[source]

Plot particle phase spaces etc of 3D data

10. March 2018 http://yt-project.org/doc/reference/api/yt.visualization.particle_plots.html#yt.visualization.particle_plots.ParticlePlot

Parameters:

  • axis (str) – ‘x’, ‘y’ or ‘z’

  • coordinate_unit (str, optional) –

  • momentum_unit (str, optional) –

  • color (str, optional) –

  • deposition (str, optional) –

  • fontsize (int, optional) –

particle_plot(x_field, y_field, z_field=None, **kwargs)[source]

Plot particle phase spaces etc of 3D data

10. March 2018 http://yt-project.org/doc/reference/api/yt.visualization.particle_plots.html#yt.visualization.particle_plots.ParticlePlot

Parameters:

  • x_field (str) – Particle field plotted on x-axis

  • y_field (str) – Particle field plotted on y-axis

  • z_field (str, optional) – Field to be displayed on the colorbar

  • x_unit (str, optional) –

  • y_unit (str, optional) –

  • z_unit (str, optional) –

  • z_log (bool, optional) –

  • color (str, optional) –

  • fontsize (int, optional) –

  • deposition (str, optional) –

projection_plot(axis, field, **kwargs)[source]

Plot a projection of 3D data

Parameters:

  • axis (str) – Is the direction ‘x’, ‘y’ or ‘z’

  • field (str) – Quantity to plot

  • unit (str, optional) – The data should be converted to (otherwise it takes the default given by the data)

  • zoom (float, optional) – Is the zoom factor (default: 1, i.e. no zoom)

  • color (str, optional) – Is the color for the time stamp and scale annotation

  • origin (str, optional) – Location of the origin of the plot coordinate system

  • method (str, optional) –

    Method of projection (‘mip’, ‘sum’, ‘integrate’)

    • ’mip’: maximum of field in the line of sight

    • ’sum’: summation of the field along the given axis

    • ’integrate’: integrate the requested field along the line of sight

  • overlay_particles (bool, optional) –

  • time (bool, optional) –

  • gridcmap (str, optional) –

  • grids (bool, optional) –

  • scale (bool, optional) –

Notes

https://yt-project.org/doc/visualizing/plots.html#slice-plots

slice_plot(normal, field, **kwargs)[source]

Plot a slice through 3D data

Parameters:

  • normal (str) – Is the direction ‘x’, ‘y’ or ‘z’ (normal)

  • field (str) – Quantity to plot

  • unit (str, optional) – The data should be converted to (otherwise it takes the default given by the data)

  • zoom (float, optional) – Is the zoom factor (default: 1, i.e. no zoom)

  • color (str, optional) – Is the color for the time stamp and scale annotation

  • origin (str, optional) – Location of the origin of the plot coordinate system

  • overlay_particles (bool, optional) –

  • time (bool, optional) –

  • gridcmap (str, optional) –

  • grids (bool, optional) –

  • scale (bool, optional) –

Notes

https://yt-project.org/doc/visualizing/plots.html#slice-plots

opal.visualization.BasePlotter module

class opal.visualization.BasePlotter.BasePlotter[source]

Bases: object

__init__()[source]

opal.visualization.FieldPlotter module

class opal.visualization.FieldPlotter.FieldPlotter[source]

Bases: BasePlotter

plot_line(field, normal, step=0, **kwargs)[source]

Do a line plot through the center. The line can only be drawn orthogonal to one of the directions x, y, or z.

Parameters:

  • field (str) – name of scalar field or vector field component

  • normal (str) – normal direction. Either ‘x’, ‘y’, or ‘z’

  • step (int, optional) – time step

  • kwargs (dict, optional) – keywords of matplotlib.pyplot.plot

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_projection(field, normal, step=0, method='integrated', **kwargs)[source]

Do a projection plot.

Parameters:

  • field (str) – name of scalar field or vector field component

  • normal (str) – normal direction. Either ‘x’, ‘y’, or ‘z’

  • step (int, optional) – time step

  • method (str, optional) – projection method: ‘integrated’, ‘sum’ or ‘max’

  • kwargs (dict, optional) – keywords of matplotlib.pyplot.pcolormesh

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_slice(field, normal, pos=0.0, index=0, step=0, **kwargs)[source]

Do a slice plot.

Parameters:

  • field (str) – name of scalar field or vector field component

  • normal (str) – normal direction. Either ‘x’, ‘y’, or ‘z’

  • pos (float, optional) – coordinate position of slice

  • step (int, optional) – time step

  • index (int, optional) – if index > 0, pos is ignored.

  • kwargs (dict, optional) – keywords of matplotlib.pyplot.pcolormesh

Returns:

Plot handle

Return type:

matplotlib.pyplot

opal.visualization.GridPlotter module

class opal.visualization.GridPlotter.GridPlotter[source]

Bases: BasePlotter

__init__()[source]
plot_grid_histogram(**kwargs)[source]

Plot a time series of the minimum, maximum and average number of grids per core.

plot_grids_per_level(**kwargs)[source]

Plot a time series of the number of grids per level and the total number of grids.

opal.visualization.H5Plotter module

class opal.visualization.H5Plotter.H5Plotter[source]

Bases: ProbePlotter

__init__()[source]
plot_classification(xvar, yvar, value, **kwargs)[source]

Classification Plot

Scatter plot where the points are colored according the value of the probability density function pdf(x, y) computed through kernel density estimation.

Parameters:

  • xvar (str) – x-axis variable to consider

  • yvar (str) – y-axis variable to consider

  • value (float) – Boundary value of classification

  • step (int, optional) – Step of dataset

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_density(xvar, yvar, **kwargs)[source]

Do a density plot.

Parameters:

  • xvar (str) – x-axis variable to consider

  • yvar (str) – y-axis variable to consider

  • step (int, optional) – Step of dataset

  • bins (array_like or int, optional) – Number of bins

  • cmap ((matplotlib.pyplot.Colormap, str), optional) – Color map

References

(22. March 2018) https://stackoverflow.com/questions/20105364/how-can-i-make-a-scatter-plot-colored-by-density-in-matplotlib

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_density_scipy(xvar, yvar, **kwargs)[source]

Do a density plot

Parameters:

  • xvar (str) – x-axis variable to consider

  • yvar (str) – y-axis variable to consider

  • step (int, optional) – Step of dataset

  • nxbin (int, optional) – Number of bins for x-axis

  • nybin (int, optional) – Number of bins for y-axis

  • cmap (str, optional) – Colormap

  • doShading (bool, optional) – If true, it uses ‘gouraud’ shading, else ‘flat’ shading

  • xlim (tuple, optional) – If not specified use data to compute limits

  • ylim (tuple, optional) – If not specified use data to compute limits

  • clabel (str, optional) – Label of colorbar

Notes

https://matplotlib.org/examples/pylab_examples/pcolor_demo.html

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_histogram(var, **kwargs)[source]

Plot a 1D histogram.

Parameters:
Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_joint(xvar, yvar, join, **kwargs)[source]

Do a joint plot (marginals + contour / scatter)

Parameters:

  • xvar (str) – x-axis variable to consider

  • yvar (str) – y-axis variable to consider

  • join (str) – ‘all’, ‘contour’ or ‘scatter’

  • step (int, optional) – Step of dataset

See also

visualization.statistics.impl_plots.plot_joint

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_phase_space(xvar, yvar, **kwargs)[source]

Plot a 2D phase space plot.

Parameters:

  • xvar (str) – Variable for x-axis

  • yvar (str) – Variable for y-axis

  • step (int, optional) – Step of dataset

  • bins (list or integer, optional) – Color energy bins

  • xscale (str, optional) – ‘linear’, ‘log’

  • yscale (str, optional) – ‘linear’, ‘log’

  • xsci (bool, optional) – x-ticks in scientific notation

  • ysci (bool, optional) – y-ticks in scientific notation

  • markersize (int) – Size of markers in scatter plot

Returns:

Plot handle

Return type:

matplotlib.pyplot

opal.visualization.MemoryPlotter module

class opal.visualization.MemoryPlotter.MemoryPlotter[source]

Bases: BasePlotter

__init__()[source]
plot_memory_boxplot(**kwargs)[source]
plot_memory_summary(**kwargs)[source]

Plot the maximum, minimum and average memory consumption vs. simulation time.

plot_total_memory(**kwargs)[source]

Plot the total memory consumption vs. simulation time.

opal.visualization.OptimizerPlotter module

class opal.visualization.OptimizerPlotter.OptimizerPlotter[source]

Bases: BasePlotter

__find(lst, key, value)
__init__()[source]
__natural_sort(l)
__sort_list(names, dimension, key)
plot_dvar_evolution(opt=0, dvars=[], op=<built-in function min>, **kwargs)[source]

Plot the evolution of the design variable values dependent on the improvement of the objectives with generation.

The operator ‘op’ is executed on two objective value sums of two individuals.

Parameters:

  • opt (int, optional) – Optimizer number (default: 0)

  • dvars (list of str, optional) – List of design variables, if not specified all are plotted

  • op (callable, optional) – Operator, e.g. max, min, etc

  • xscale (str, optional) – ‘linear’, ‘log’

  • yscale (str, optional) – ‘linear’, ‘log’

  • grid (bool, optional) –

plot_individual_bounds(n, opt=0, **kwargs)[source]

Plot all design variables and their bounds.

This will show if a design variable is close to one of its bounds.

Parameters:

  • n (int) – Take the first n-th best individuals

  • opt (int, optional) – Optimizer number (default: 0)

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_objective_evolution(opt=0, objs=[], op=<built-in function min>, **kwargs)[source]

Plot the improvement of the objectives with generation.

The operator ‘op’ is executed between individuals per population

Parameters:

  • opt (int, optional) – Optimizer number (default: 0)

  • objs (list of str, optional) – List of objectives, if not specified all are plotted

  • op (callable, optional) – Operator, e.g. max, min, etc

  • xscale (str, optional) – ‘linear’, ‘log’

  • yscale (str, optional) – ‘linear’, ‘log’

  • grid (bool, optional) –

  • total (bool, optional) – Show sum of objectives

  • label_rep (dict, optional) – Replace labels by

  • as_bar (bool, optional) –

  • colorlist (list of str, optional) –

plot_objectives(opt=0, **kwargs)[source]

Plotting function for multiobjective optimizer output.

Show the trend of the sum of the objectives with the generation.

Parameters:

  • opt (int, optional) – Optimizer number (default: 0)

  • xscale (str, optional) – ‘linear’ or ‘log’, default: linear

  • yscale (str, optional) – ‘linear’ or ‘log’, default: linear

  • grid (bool, optional) – Show grid, default: False

  • avg (bool, optional) – Take averaged sum over all objectives default: true

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_parallel_coordinates(gen, opt=0, **kwargs)[source]

Plotting function for multiobjective optimizer output.

Parameters:

  • gen (int) – Generation to plot

  • opt (int, optional) – Optimizer number (default: 0)

Notes

(30. April 2018) https://plot.ly/python/static-image-export/ https://plot.ly/python/parallel-coordinates-plot/ https://stackoverflow.com/questions/40243446/how-to-save-plotly-offline-graph-in-format-png

plot_pareto_front(xdvar, ydvar, opt=0, **kwargs)[source]

Plot the Pareto front

Parameters:

  • xdvar (str) – Design variable on x-axis

  • ydvar (str) – Design variable on y-axis

  • opt (int, optional) – Optimizer number (default: 0)

Returns:

Plot handle

Return type:

matplotlib.pyplot

opal.visualization.PeakPlotter module

class opal.visualization.PeakPlotter.PeakPlotter[source]

Bases: BasePlotter

__init__()[source]
plot_peak_difference(dset, **kwargs)[source]

Plot the peak difference of a probe output.

Parameters:

  • dset (PeakDataset) – A dataset

  • grid (bool, optional) – Draw grid

  • raxis (bool, optional) – Do radius vs radius plot instead

  • begin (int, optional) – First peak

  • end (int, optional) – Last peak

Returns:

Plot handle

Return type:

matplotlib.pyplot

opal.visualization.ProbePlotter module

class opal.visualization.ProbePlotter.ProbePlotter[source]

Bases: BasePlotter

__init__()[source]
plot_probe_histogram(**kwargs)[source]

Plot a histogram of the probe histogram bin count vs. radius.

Parameters:

  • grid (bool, optional) – Draw grid

  • scale (bool, optional) – Scales to 1.0 (default: False)

  • bunch (int, optional) – Bunch number (default: 0)

  • begin (int, optional) – Start step (default: 0)

  • end (int, optional) – End step (default: ds.size)

  • **kwargs – In case of H5: additional arguments passed to matplotlib.pyplot.hist

Returns:

Plot handle

Return type:

matplotlib.pyplot

opal.visualization.ProfilingPlotter module

class opal.visualization.ProfilingPlotter.ProfilingPlotter[source]

Bases: BasePlotter

__init__()[source]
plot_lbal_boxplot(**kwargs)[source]

Particle load balancing.

Does a (simulation) time series boxplot of the particle load balancing.

plot_lbal_histogram(**kwargs)[source]

Particle load balancing.

Plots the time series (i.e. simulation time) histogram with the number of cores having the same amount of particles. The user can specify ranges givin the upper and lower boundary, i.e. ‘bupper’ and, respectively, ‘blower’. Those boundaries are given in percent.

plot_lbal_summary(**kwargs)[source]

Particle load balancing.

Plot the minimum, maximum and average number of particles per core vs. the simulation time.

opal.visualization.SamplerPlotter module

class opal.visualization.SamplerPlotter.SamplerPlotter[source]

Bases: BasePlotter

__init__()[source]
_autolabel(ax, rects, xpos='center')[source]

Attach a text label above each bar in rects, displaying its height.

Copied from matplotlib.org. It’s slightly modified.

xpos indicates which side to place the text w.r.t. the center of the bar. It can be one of the following {‘center’, ‘right’, ‘left’}.

plot_auto_correlation(ind, **kwargs)[source]

Compare a sample set with itself.

Parameters:

  • ind (list) – Indices of the sample set.

  • nsamples (bool, optional) – Show a horizontal line indicating the total number of samples

  • percent (bool, optional) – Indicate the agreement in percent above each bar

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_sample_input_statistics(**kwargs)[source]

Bar plot showing the number of samples per design variable. This makes only sense for sampling with only a few states.

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_training_vs_validation(train0, **kwargs)[source]

Bar plot comparing training with validation set.

Parameters:

  • train0 (list) – Indices of the training points.

  • train1 (list, optional) – More lists with indices train2, train3, etc. are also keywords

  • nsamples (bool, optional) – Show a horizontal line indicating the total number of samples

  • percent (bool, optional) – Indicate the agreement in percent above each bar

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_variability(fname, xvar, yvar, **kwargs)[source]

Plot the mean, min and max over all samples.

Parameters:

  • fname (str) – File containing the data (xvar and yvar)

  • xvar (str) – x-axis data

  • yvar (str) – y-axis data

  • idx (bool, optional) – Fix the x-axis labels (takes the original data order but uses the indices to plot and the values as ticks), useful for periodic values (e.g. azimuth)

  • nticks (int, optional) – Number of ticks on axes (only for idx=True)

Returns:

Plot handle

Return type:

matplotlib.pyplot

opal.visualization.SolverPlotter module

class opal.visualization.SolverPlotter.SolverPlotter[source]

Bases: BasePlotter

__init__()[source]
plot_solver_histogram(var, **kwargs)[source]

Plot a time series of solver output, e.g. error, number of iterations, etc.

opal.visualization.StatPlotter module

class opal.visualization.StatPlotter.StatPlotter[source]

Bases: BasePlotter

__init__()[source]
plot_envelope(xvar='position', **kwargs)[source]

Create an envelope plot.

Author: Philippe Ganz Date: 2018

Parameters:

  • xvar (str) – x-axis variable

  • dset (list [StatDataset], optional) – List of other statistic datasets

  • lfile (str, optional) – Lattice file (*.lattice)

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_profile1D(xvar, yvar, **kwargs)[source]

Plot a 1D profile.

Parameters:

  • xvar (str) – Variable for x-axis

  • yvar (str) – Variable for y-axis

  • xscale (str, optional) – ‘linear’, ‘log’

  • yscale (str, optional) – ‘linear’, ‘log’

  • xsci (bool, optional) – x-ticks in scientific notation

  • ysci (bool, optional) – y-ticks in scientific notation

Returns:

Plot handle

Return type:

matplotlib.pyplot

opal.visualization.StdOpalOutputPlotter module

class opal.visualization.StdOpalOutputPlotter.StdOpalOutputPlotter[source]

Bases: TimingPlotter

__init__()[source]
plot_RF_phases(RFcavity, **kwargs)[source]
Parameters:

RFcavity (list [str]) – List of names of the RFcavity as specifed in the input file

Returns:

Plot handle

Return type:

matplotlib.pyplot

opal.visualization.TimingPlotter module

class opal.visualization.TimingPlotter.TimingPlotter[source]

Bases: BasePlotter

__init__()[source]
__mostConsuming(n, times, labels, prop)

Retturn time and label of the first n most time consuming timings.

Parameters:

  • timings (labels ([]) list of labels to appropriate) –

  • data (times ([]) list of timing) –

  • timings –

Return type:

sorted times and labels

plot_efficiency(dsets, what, prop, **kwargs)[source]

Efficiency plot of a timing benchmark study

E_p = S_p / p

where E_p is the efficiency and S_p the speed-up with p cores / nodes.

Parameters:

  • datasets (dsets ([TimeDataset]) all timing) –

  • name (what (str) timing) –

  • property (prop (str)) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

  • avg' (i.e. 'cpu) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

  • max' ('cpu) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

  • min' ('cpu) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

:param‘wall avg’, ‘wall max’, ‘wall min’ or

‘cpu tot’ and ‘wall tot’ (only for main timing)

Parameters:

  • Optionals –

  • --------- –

  • scale (yscale (str) y-axis) –

  • 'log' ('linear' or) –

  • scale –

  • 'log' –

  • true (grid (bool) if) –

  • grid (plot) –

  • percentage (percent (bool) efficiency in) –

  • '#cores' (xlabel (str) label for x-axis. Default) –

  • node (core2node (int) scale #cores == 1) – (useful with xlabel=’#nodes’)

Return type:

a matplotlib.pyplot handle

plot_pie_chart(prop, **kwargs)[source]

Create a pie plot of the first N most time consuming timings.

Parameters:

  • dataset (ds (DatasetBase) timing) –

  • property (prop (str)) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

  • avg' (i.e. 'cpu) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

  • max' ('cpu) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

  • min' ('cpu) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

:param‘wall avg’, ‘wall max’, ‘wall min’ or

‘cpu tot’ and ‘wall tot’ (only for main timing)

Parameters:

  • Optionals –

  • --------- –

  • specialized (first=None (int) take only the first N) – timings

  • timings (exclude ([]) do not use these) –

  • name (tag='' (str) what tag should be in) –

  • scheme (cmap_name='YlGn' (str) color) –

Notes

Throws an exception if file not available or the key is not part of the dictionary

Return type:

a matplotlib.pyplot handle

plot_speedup(dsets, what, prop, **kwargs)[source]

Speedup plot of a timing benchmark study

S_p = T_1 / T_p

where T_1 is the time for a single core run (or reference run with several cores / nodes) and T_p the time with p cores. S_p then represents the speed-up with p cores / nodes.

Parameters:

  • datasets (dsets ([TimeDataset]) all timing) –

  • name (what (str) timing) –

  • property (prop (str)) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

  • avg' (i.e. 'cpu) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

  • max' ('cpu) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

  • min' ('cpu) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)

:param‘wall avg’, ‘wall max’, ‘wall min’ or

‘cpu tot’ and ‘wall tot’ (only for main timing)

Parameters:

  • Optionals –

  • --------- –

  • scale (yscale (str) y-axis) –

  • 'log' ('linear' or) –

  • scale –

  • 'log' –

  • true (grid (bool) if) –

  • grid (plot) –

  • plot (efficiency (bool) add efficiency to) –

  • '#cores' (xlabel (str) label for x-axis. Default) –

  • node (core2node (int) scale #cores == 1) – (useful with xlabel=’#nodes’)

  • line (perfect_scaling (bool) add speed-up perfect scaling) –

Return type:

a matplotlib.pyplot handle

plot_time_scaling(dsets, prop, **kwargs)[source]

Plot timing benchmark.

Parameters:

  • datasets (dsets ([TimeDataset]) all timing) –

  • property (prop (str)) –

  • 'cpu ('wall' or) –

  • Optionals –

  • --------- –

  • specialized (first=None (int) take only the first N) –

  • scale (yscale (str) y-axis) –

  • 'log' ('linear' or) –

  • scale –

  • 'log' –

  • true (grid (bool) if) –

  • grid (plot) –

  • '#cores' (xlabel (str) label for x-axis. Default) –

  • node (core2node (int) scale #cores == 1) – (useful with xlabel=’#nodes’)

  • timings (exclude ([]) do not use these) –

  • tag (tag='' (str) take only timings containing this) –

  • line (perfect_scaling (bool) add speed-up perfect scaling) –

Return type:

a matplotlib.pyplot handle

plot_time_summary(prop, **kwargs)[source]

Create a plot with minimum, maximum and average timings

Parameters:

  • dataset (ds (DatasetBase) timing) –

  • property (prop (str)) –

  • 'cpu ('wall' or) –

  • Optionals –

  • --------- –

  • scale (yscale (str) y-axis) –

  • 'log' ('linear' or) –

  • true (grid (bool) if) –

  • grid (plot) –

  • timings (exclude ([]) do not use these) –

  • tag (tag='' (str) take only timings containing this) –

Return type:

a matplotlib.pyplot handle

opal.visualization.TrackOrbitPlotter module

class opal.visualization.TrackOrbitPlotter.TrackOrbitPlotter[source]

Bases: BasePlotter

__init__()[source]
plot_beta_beat(nsteps=-1, **kwargs)[source]
Parameters:

nsteps (int, optional) – Number of steps per turn (default -1: detect automatically)

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_centering(**kwargs)[source]
Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_energy(nsteps=-1, **kwargs)[source]
Parameters:

nsteps (int, optional) – Number of steps per turn (default -1: detect automatically)

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_energy_gain(nsteps=-1, **kwargs)[source]
Parameters:

nsteps (int, optional) – Number of steps per turn (default -1: detect automatically)

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_orbits(pid=0, **kwargs)[source]

Do an orbit plot.

Parameters:

pid (int, optional) – Which particle id (default: 0)

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_turn_separation(nsteps=-1, angle=0.0, asFunctionOfTurnNumber=True, asFunctionOfEnergy=False, **kwargs)[source]
Parameters:

  • nsteps (int, optional) – Number of steps per turn (default -1: detect automatically)

  • angle (float, optional) – Angle of reference line in radians

  • asFunctionOfTurnNumber (bool) – x-axis turn number

  • asFunctionOfEnergy (bool) – x-axis energy

Returns:

Plot handle

Return type:

matplotlib.pyplot

plot_turns(**kwargs)[source]
Returns:

Plot handle

Return type:

matplotlib.pyplot

opal.visualization.formatter module