opal.visualization package¶
Notebooks¶
- Load Dataset
- Projection Plot
- Slice Plot
- Line Plot
- Particle Plot
- Overlay Particles onto Fields
- H5Plotter_RingMultiBunch
- ProbePlotter
- ProbePlotter_PeakFile
- OptimizerPlotter
- SamplerPlotter
- StatPlotter_FODO
- StatPlotter_Gantry
- StatPlotter_RingCyclotron
- Load the RingCyclotron.stat file
- Set the plotting style
- Do some plotting …
- Load the RingCyclotron.h5 file
- Do some plotting …
- Do some statistics on data
- Load the RingCyclotron.mem file
- Do some plotting …
- Load the RingCyclotron.lbal file
- Do some plotting …
- Load the RingCyclotron-trackOrbit file
- Do some plotting …
- Load timing.dat file
- Do some plotting …
opal.visualization.AmrPlotter module¶
-
class
opal.visualization.AmrPlotter.
AmrPlotter
[source]¶ Bases:
opal.visualization.BasePlotter.BasePlotter
-
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) –
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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¶
opal.visualization.FieldPlotter module¶
-
class
opal.visualization.FieldPlotter.
FieldPlotter
[source]¶ Bases:
opal.visualization.BasePlotter.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]¶
opal.visualization.H5Plotter module¶
-
class
opal.visualization.H5Plotter.
H5Plotter
[source]¶ Bases:
opal.visualization.ProbePlotter.ProbePlotter
-
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
var (str) – Variable to consider
step (int, optional) – Step of dataset
bins (int or str, optional) – Binning type or number of bins (see https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.histogram.html)
density (bool, optional) –
- Normalize such that integral over
range is 1.
- 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]¶
opal.visualization.OptimizerPlotter module¶
-
class
opal.visualization.OptimizerPlotter.
OptimizerPlotter
[source]¶ Bases:
opal.visualization.BasePlotter.BasePlotter
-
__find
(lst, key, value)¶
-
__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
-
opal.visualization.PeakPlotter module¶
-
class
opal.visualization.PeakPlotter.
PeakPlotter
[source]¶ Bases:
opal.visualization.BasePlotter.BasePlotter
-
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:
opal.visualization.BasePlotter.BasePlotter
-
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:
opal.visualization.BasePlotter.BasePlotter
-
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.
-
opal.visualization.SamplerPlotter module¶
-
class
opal.visualization.SamplerPlotter.
SamplerPlotter
[source]¶ Bases:
opal.visualization.BasePlotter.BasePlotter
-
_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¶
opal.visualization.StatPlotter module¶
-
class
opal.visualization.StatPlotter.
StatPlotter
[source]¶ Bases:
opal.visualization.BasePlotter.BasePlotter
-
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¶
opal.visualization.TimingPlotter module¶
-
class
opal.visualization.TimingPlotter.
TimingPlotter
[source]¶ Bases:
opal.visualization.BasePlotter.BasePlotter
-
__mostConsuming
(n, times, labels, prop)¶ Retturn time and label of the first n most time consuming timings.
- Parameters
(int) number of timings (n) –
([]) list of timing data (times) –
([]) list of labels to appropriate timings (labels) –
- Returns
- 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
([TimeDataset]) all timing datasets (dsets) –
(str) timing name (what) –
(str) property (prop) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)
'cpu avg' (i.e.) – ‘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 –
--------- –
(str) x-axis scale (xscale) –
or 'log' ('linear') –
(str) y-axis scale (yscale) –
or 'log' –
(bool) if true (grid) –
grid (plot) –
(bool) efficiency in percentage (percent) –
(str) label for x-axis. Default '#cores' (xlabel) –
(int) scale #cores == 1 node (core2node) – (useful with xlabel=’#nodes’)
- Returns
- 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
(DatasetBase) timing dataset (ds) –
(str) property (prop) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)
'cpu avg' (i.e.) – ‘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 –
--------- –
(int) take only the first N specialized (first=None) – timings
([]) do not use these timings (exclude) –
(str) what tag should be in name (tag='') –
(str) color scheme (cmap_name='YlGn') –
Notes
Throws an exception if file not available or the key is not part of the dictionary
- Returns
- 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
([TimeDataset]) all timing datasets (dsets) –
(str) timing name (what) –
(str) property (prop) – ‘wall avg’, ‘wall max’, ‘wall min’ or ‘cpu tot’ and ‘wall tot’ (only for main timing)
'cpu avg' (i.e.) – ‘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 –
--------- –
(str) x-axis scale (xscale) –
or 'log' ('linear') –
(str) y-axis scale (yscale) –
or 'log' –
(bool) if true (grid) –
grid (plot) –
(bool) add efficiency to plot (efficiency) –
(str) label for x-axis. Default '#cores' (xlabel) –
(int) scale #cores == 1 node (core2node) – (useful with xlabel=’#nodes’)
(bool) add speed-up perfect scaling line (perfect_scaling) –
- Returns
- Return type
a matplotlib.pyplot handle
-
plot_time_scaling
(dsets, prop, **kwargs)[source]¶ Plot timing benchmark.
- Parameters
([TimeDataset]) all timing datasets (dsets) –
(str) property (prop) –
or 'cpu ('wall') –
Optionals –
--------- –
(int) take only the first N specialized (first=None) –
(str) x-axis scale (xscale) –
or 'log' ('linear') –
(str) y-axis scale (yscale) –
or 'log' –
(bool) if true (grid) –
grid (plot) –
(str) label for x-axis. Default '#cores' (xlabel) –
(int) scale #cores == 1 node (core2node) – (useful with xlabel=’#nodes’)
([]) do not use these timings (exclude) –
(str) take only timings containing this tag (tag='') –
(bool) add speed-up perfect scaling line (perfect_scaling) –
- Returns
- Return type
a matplotlib.pyplot handle
-
plot_time_summary
(prop, **kwargs)[source]¶ Create a plot with minimum, maximum and average timings
- Parameters
(DatasetBase) timing dataset (ds) –
(str) property (prop) –
or 'cpu ('wall') –
Optionals –
--------- –
(str) y-axis scale (yscale) –
or 'log' ('linear') –
(bool) if true (grid) –
grid (plot) –
([]) do not use these timings (exclude) –
(str) take only timings containing this tag (tag='') –
- Returns
- Return type
a matplotlib.pyplot handle
-
opal.visualization.TrackOrbitPlotter module¶
-
class
opal.visualization.TrackOrbitPlotter.
TrackOrbitPlotter
[source]¶ Bases:
opal.visualization.BasePlotter.BasePlotter
-
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_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
-