# Post-process¶

This page describes the usage of the python module happi for extracting, viewing and post-processing simulation data. First, you need to install happi.

The module can be imported directly in python:

import happi


## Open a simulation¶

In a python command line (or script), call the following function to open your Smilei simulation. Note that several simulations can be opened at once, as long as they correspond to several restarts of the same simulation.

happi.Open(results_path='.', show=True, reference_angular_frequency_SI=None, verbose=True)
• results_path: path or list of paths to the directory-ies where the results of the simulation-s are stored. It can also contain wildcards, such as * and ? in order to include several simulations at once.

• reference_angular_frequency_SI: overrides the value of the simulation parameter reference_angular_frequency_SI, in order to re-scale units.

• show: if False, figures will not plot on screen. Make sure that you have not loaded another simulation or the matplotlib package. You may need to restart python.

• verbose: if False, less information is printed while post-processing.

• scan: if False, HDF5 output files are not scanned initially.

Returns: An object containing various methods to extract and manipulate the simulation

outputs, as described below.

Example:

S = happi.Open("path/to/my/results")


Once a simulation is opened, several methods are available to find information on the namelist or open various diagnostics. Checkout the namelist documentation to find out which diagnostics are included in Smilei: scalars, fields, probes, particle binning, trajectories and performances.

## Extract namelist information¶

Once a simulation is opened as shown above, you can access the content of the namelist using the attribute namelist:

S = happi.Open("path/to/my/results") # Open a simulation
print(S.namelist.Main.timestep)   # print the timestep
print(S.namelist.Main.geometry)   # print the simulation dimensions


All the variables defined in the original namelist are copied into this variable.

Concerning components like Species, External fields or Probe diagnostics, of which several instances may exist, you can directly iterate over them:

for species in S.namelist.Species:
print("species "+species.name+" has mass "+str(species.mass))


You can also access to a specific component by referencing its number:

F = S.namelist.ExternalField[0]  # get the first external field
print("An external field "+F.field+" was applied")


In the case of the species, you can also obtain a given species by its name:

species = S.namelist.Species["electron1"]
print("species "+species.name+" has mass "+str(species.mass))


## Obtain diagnostic information¶

Print available diagnostics

Commands S.Scalar, S.Field, S.Probe (etc.) will display general information about the corresponding diagnostics in the simulation.

List available diagnostics

getDiags(diagType)

Returns a list of available diagnostics of the given type

• diagType: The diagnostic type ("Field", "Probe", etc.)

getTrackSpecies()

Returns a list of available tracked species.

Information on specific diagnostics

fieldInfo(diag)
• diag: the number or name of a Field diagnostic

Returns a dictionnary containing:

• "diagNumber": the diagnostic number

• "diagName": the diagnostic name

• "fields": list of the available fields in this diagnostic. In the case of AMcylindrical geometry, this is a dictionnary with a list of modes for each field.

probeInfo(diag)
• diag: the number or name of a Probe diagnostic

Returns a dictionnary containing:

• "probeNumber": the diagnostic number

• "probeName": the diagnostic name

• "fields": list of the available fields in this diagnostic

performanceInfo()

Returns a dictionnary containing:

• "quantities_uint": a list of the available integer quantities

• "quantities_double": a list of the available float quantities

• "patch_arrangement": the type of patch arrangement

• "timesteps": the list of timesteps

## Open a Scalar diagnostic¶

Scalar(scalar=None, timesteps=None, units=[''], data_log=False, data_transform=None, **kwargs)
• scalar: The name of the scalar.
If not given, then a list of available scalars is printed.
• timesteps: The requested timestep(s).
If omitted, all timesteps are used.
If one number given, the nearest timestep available is used.
If two numbers given, all the timesteps in between are used.
• units: A unit specification (see Specifying units)

• data_log:
If True, then $$\log_{10}$$ is applied to the output.
• data_transform:
If this is set to a function, the function is applied to the output before plotting.

Example:

S = happi.Open("path/to/my/results")
Diag = S.Scalar("Utot")


## Open a Field diagnostic¶

Field(diagNumber=None, field=None, timesteps=None, subset=None, average=None, units=[''], data_log=False, data_transform=None, moving=False, export_dir=None, **kwargs)
• timesteps, units, data_log, data_transform: same as before.

• diagNumber: number or name of the fields diagnostic
If not given, then a list of available diagnostic numbers is printed.
• field: The name of a field ("Ex", "Ey", etc.)
If not given, then a list of available fields is printed.
The string can also be an operation between several fields, such as "Jx+Jy".
• subset: A selection of coordinates to be extracted.
Syntax 1: subset = { axis : location, ... }
Syntax 2: subset = { axis : [start, stop] , ... }
Syntax 3: subset = { axis : [start, stop, step] , ... }
axis must be "x", "y" , "z" or "r".
Only the data within the chosen axes’ selections is extracted.
WARNING: THE VALUE OF step IS A NUMBER OF CELLS.
Example: subset = {"y":[10, 80, 4]}
• average: A selection of coordinates on which to average.
Syntax 1: average = { axis : "all", ... }
Syntax 2: average = { axis : location, ... }
Syntax 3: average = { axis : [start, stop] , ... }
axis must be "x", "y" , "z" or "r".
The chosen axes will be removed:
- With syntax 1, an average is performed over all the axis.
- With syntax 2, only the bin closest to location is kept.
- With syntax 3, an average is performed from start to stop.
Example: average = {"x":[4,5]} will average for $$x$$ within [4,5].
• moving: If True, plots will display the X coordinates evolving according to the moving window

• export_dir: The directory where to export VTK files.

In the case of an azimuthal mode cylindrical geometry (AMcylindrical), additional argument are available. You must choose one of theta or build3d, defined below, in order to construct fields from their complex angular Fourier modes. In addition, the modes argument is optional.

• theta: An angle (in radians)
Calculates the field in a plane passing through the $$r=0$$ axis
and making an angle theta with the $$xy$$ plane.
• build3d: A list of three ranges
Calculates the field interpolated in a 3D $$xyz$$ grid.
Each range is a list [start, stop, step] indicating the beginning,
the end and the step of this grid.
• modes: An integer or a list of integers
Only these modes numbers will be used in the calculation. If omited, all modes are used.

Example:

S = happi.Open("path/to/my/results")
Diag = S.Field(0, "Ex", average = {"x":[4,5]}, theta=math.pi/4.)


## Open a Probe diagnostic¶

Probe(probeNumber=None, field=None, timesteps=None, subset=None, average=None, units=[''], data_log=False, data_transform=None, **kwargs)
• timesteps, units, data_log, data_transform, export_dir: same as before.

• probeNumber: number or name of the probe (the first one has number 0).
If not given, a list of available probes is printed.
• field: name of the field ("Bx", "By", "Bz", "Ex", "Ey", "Ez", "Jx", "Jy", "Jz" or "Rho").
If not given, a list of available fields is printed.
The string can also be an operation between several fields, such as "Jx+Jy".
• subset and average are very similar to those of Field(), but they can only have the axes: "axis1", "axis2" and "axis3". For instance, average={"axis1":"all"}. Note that the axes are not necessarily $$x$$, $$y$$ or $$z$$ because the probe mesh is arbitrary.

Example:

S = happi.Open("path/to/my/results")
Diag = S.Probe(0, "Ex")


## Open a ParticleBinning diagnostic¶

ParticleBinning(diagNumber=None, timesteps=None, subset=None, average=None, units=[''], data_log=False, data_transform=None, **kwargs)
• timesteps, units, data_log, data_transform, export_dir: same as before.

• diagNumber: number or name of the particle binning diagnostic (starts at 0).
If not given, a list of available diagnostics is printed.
It can also be an operation between several diagnostics.
For example, "#0/#1" computes the division by diagnostics 0 and 1.
• subset is similar to that of Field(), although the axis must be one of

"x", "y", "z", "px", "py", "pz", "p", "gamma", "ekin", "vx", "vy", "vz", "v" or "charge".

WARNING: With the syntax subset={axis:[start, stop, step]}, the value of step is a number of bins.

• average: a selection of coordinates on which to average the data.
Syntax 1: average = { axis : "all", ... }
Syntax 2: average = { axis : location, ... }
Syntax 3: average = { axis : [begin, end] , ... }

axis must be "x", "y", "z", "px", "py", "pz", "p", "gamma", "ekin", "vx", "vy", "vz", "v" or "charge".

The chosen axes will be removed:
- With syntax 1, an average is performed over all the axis.
- With syntax 2, only the bin closest to location is kept.
- With syntax 3, an average is performed between begin and end.
Example: average={"x":[4,5]} will average all the data for x within [4,5].

Example:

S = happi.Open("path/to/my/results")
Diag = S.ParticleBinning(1)


Units of the results:

The raw quantity stored in the output file has the units of the deposited_quantity. Generally, this is a sum of macro-particle weights. As those weights are not in units of density (but of density multiplied by hypervolume), a correction is applied in happi: it divides the data by an hypervolume. More precisely, for each direction x, y or z, if this direction is not included in one of the diagnostic’s axes, happi divides by the length of the box in that direction.

In addition, in order to make the units relative to the bin size, happi divides the data in each bin by the bin size.

## Open a Screen diagnostic¶

Screen(diagNumber=None, timesteps=None, subset=None, average=None, units=[''], data_log=False, data_transform=None, **kwargs)
• timesteps, units, data_log, data_transform, export_dir: same as before.

• diagNumber, subset and average: identical to that of ParticleBinning diagnostics.

Example:

S = happi.Open("path/to/my/results")
Diag = S.Screen(0)


ParticleBinning(diagNumber=None, timesteps=None, subset=None, average=None, units=[''], data_log=False, data_transform=None, **kwargs)
• timesteps, units, data_log, data_transform, export_dir: same as before.

• diagNumber, subset and average: identical to that of ParticleBinning diagnostics.

Example:

S = happi.Open("path/to/my/results")


Note

The resulting spectral power is in units of $$\omega_r$$. If additional axes are used, the power spectrum is divided by the size of the bins of each axes.

## Open a TrackParticles diagnostic¶

TrackParticles(species=None, select='', axes=[], timesteps=None, sort=True, length=None, units=[''], **kwargs)
• timesteps, units, export_dir: same as before.

• species: the name of a tracked-particle species. If omitted, a list of available tracked-particle species is printed.

• select: Instructions for selecting particles among those available. A detailed explanation is provided below

• axes: A list of axes for plotting the trajectories or obtaining particle data.

Each axis is one of the attributes defined in the namelist. In addition, when there is a moving window, the axis "moving_x" is automatically available.

Example: axes = ["x"] corresponds to $$x$$ versus time.
Example: axes = ["x","y"] correspond to 2-D trajectories.
Example: axes = ["x","px"] correspond to phase-space trajectories.
• sort: may be either

• False: the particles are not sorted by ID. This can save significant time, but prevents plotting, exporting to VTK, and the select argument. Only getData and iterParticles are available in this mode. Read this for more information on particle IDs.

• True: the particles are sorted in a new file, unless this file already exists. If it does, sorted particles are directly read from the sorted file.

• A string for selecting particles (same syntax as select): only selected particles are sorted in a new file. The file name must be defined in the argument sorted_as.

• sorted_as: a keyword that defines the new sorted file name (when sort is a selection) or refers to a previously user-defined sorted file name (when sort is not given).

• length: The length of each plotted trajectory, in number of timesteps.

Example:

S = happi.Open("path/to/my/results")
Diag = S.TrackParticles("electrons", axes=["px","py"])


Detailed explanation of the select parameter

Say times is a condition on timesteps t, for instance t>50.
Say condition is a condition on particles properties (x, y, z, px, py, pz), for instance px>0.
• Syntax 1: select="any(times, condition)"
Selects particles satisfying condition for at least one of the times.
For example, select="any(t>0, px>1.)" selects those reaching $$p_x>1$$ at some point.
• Syntax 2: select="all(times, condition)"
Selects particles satisfying condition at all times.
For example, select="all(t<40, px<1)" selects those having $$p_x<1$$ until timestep 40.
• Syntax 3: select=[ID1, ID2, ...]
Selects the provided particle IDs.
• It is possible to make logical operations: + is OR; * is AND; ~ is NOT.
For example, select="any((t>30)*(t<60), px>1) + all(t>0, (x>1)*(x<2))"

## Open a Performances diagnostic¶

The post-processing of the performances diagnostic may be achieved in three different modes: raw, map, or histogram, described further below. You must choose one and only one mode between those three.

Performances(raw=None, map=None, histogram=None, timesteps=None, units=[''], data_log=False, data_transform=None, species=None, cumulative=True, **kwargs)
• timesteps, units, data_log, data_transform, export_dir: same as before.

• raw: The name of a quantity, or an operation between them (see quantities below). The requested quantity is listed for each process.

• map: The name of a quantity, or an operation between them (see quantities below). The requested quantity is mapped vs. space coordinates (1D and 2D only).

• histogram: the list ["quantity", min, max, nsteps]. Makes a histogram of the requested quantity between min an max, with nsteps bins. The "quantity" may be an operation between the quantities listed further below.

• cumulative: may be True for timers accumulated for the duration of the simulation, or False for timers reset to 0 at each output.

Quantities at the MPI-process level (contain many patches):

• hindex : the starting index of each proc in the hilbert curve

• number_of_cells : the number of cells in each proc

• number_of_particles : the total number of non-frozen macro-particles in each proc (includes all species)

• number_of_frozen_particles : the number of frozen particles in each proc

• total_load : the load of each proc (number of macro-particles and cells weighted by cell_load coefficients)

• timer_global : global simulation time (only available for proc 0)

• timer_particles : time spent computing particles by each proc

• timer_maxwell : time spent solving maxwell by each proc

• timer_envelope : time spent solving the envelope propagation by each proc

• timer_densities : time spent projecting densities by each proc

• timer_collisions : time spent computing collisions by each proc

• timer_movWindow : time spent handling the moving window by each proc

• timer_loadBal : time spent balancing the load by each proc

• timer_partMerging : time spent merging particles by each proc

• timer_syncPart : time spent synchronzing particles by each proc

• timer_syncField : time spent synchronzing fields by each proc

• timer_syncDens : time spent synchronzing densities by each proc

• timer_syncSusceptibility : time spent synchronzing susceptibility by each proc

• timer_diags : time spent by each proc calculating and writing diagnostics

• timer_total : the sum of all timers above (except timer_global)

• memory_total : the total memory (RSS) used by the process in GB

• memory_peak : the peak memory (peak RSS) used by the process in GB

WARNING: The timers loadBal and diags include global communications. This means they might contain time doing nothing, waiting for other processes. The sync*** timers contain proc-to-proc communications, which also represents some waiting time.

Quantities at the patch level:

This requires patch_information in the namelist.

• mpi_rank : the MPI rank that contains the current patch

• vecto : the mode of the specified species in the current patch (vectorized of scalar) when the adaptive mode is activated. Here the species argument has to be specified.

WARNING: The patch quantities are only compatible with the raw mode and only in 3Dcartesian geometry. The result is a patch matrix with the quantity on each patch.

Example: performance diagnostic at the MPI level:

S = happi.Open("path/to/my/results")


Example: performance diagnostic at the patch level:

S = happi.Open("path/to/my/results")
Diag = S.Performances(raw="vecto", species="electron")


## Specifying units¶

By default, all the diagnostics data is in code units (see Units).

To change the units, all the methods Scalar(), Field(), Probe(), ParticleBinning() and TrackParticles() support a units argument. It has three different syntaxes:

1. A list, for example units = ["um/ns", "feet", "W/cm^2"]

In this case, any quantity found to be of the same dimension as one of these units will be converted.

2. A dictionary, for example units = {"x":"um", "y":"um", "v":"Joule"}

In this case, we specify the units separately for axes x and y, and for the data values v.

3. A Units object, for example units = happi.Units("um/ns", "feet", x="um")

This version combines the two previous ones.

Requirements for changing units

## Other arguments for diagnostics¶

All diagnostics above can use additional keyword arguments (kwargs) to manipulate the plotting options:

• figure: The figure number that is passed to matplotlib.

• vmin, vmax: data value limits.

• vsym: makes data limits symmetric about 0 (vmin and vmax are ignored), and sets the colormap to smileiD.

• If vsym = True, autoscale symmetrically.

• If vsym is a number, limits are set to [-vsym, vsym].

• xmin, xmax, ymin, ymax: axes limits.

• xfactor, yfactor: factors to rescale axes.

• side: "left" (by default) or "right" puts the y-axis on the left- or the right-hand-side.

• transparent: None (by default), "over", "under", "both", or a function. The colormap becomes transparent over, under, or outside both the boundaries set by vmin and vmax. This argument may be set instead to a function mapping the data value $$\in [0,1]$$ to the transparency $$\in [0,1]$$. For instance lambda x: 1-x.

• Many Matplotlib arguments listed in Advanced plotting options.

## Obtain the data¶

Scalar.getData(timestep=None)
Field.getData(timestep=None)
Probe.getData(timestep=None)
ParticleBinning.getData(timestep=None)
Screen.getData(timestep=None)
TrackParticles.getData(timestep=None)

Returns a list of the data arrays (one element for each timestep requested). In the case of TrackParticles, this method returns a dictionary containing one entry for each axis, and if sort==False, these entries are included inside an entry for each timestep.

• timestep, if specified, is the only timestep number that is read and returned.

Example:

S = happi.Open("path/to/results") # Open the simulation
Diag = S.Field(0, "Ex")       # Open Ex in the first Field diag
result = Diag.getData()       # Get list of Ex arrays (one for each time)

Scalar.getTimesteps()
Field.getTimesteps()
Probe.getTimesteps()
ParticleBinning.getTimesteps()
Screen.getTimesteps()
TrackParticles.getTimesteps()

Returns a list of the timesteps requested.

Scalar.getTimes()
Field.getTimes()
Probe.getTimes()
ParticleBinning.getTimes()
Screen.getTimes()
TrackParticles.getTimes()

Returns the list of the times requested. By default, times are in the code’s units, but are converted to the diagnostic’s units defined by the units argument, if provided.

Scalar.getAxis(axis)
Field.getAxis(axis)
Probe.getAxis(axis)
ParticleBinning.getAxis(axis)
Screen.getAxis(axis)

Returns the list of positions of the diagnostic data along the requested axis. If the axis is not available, returns an empty list. By default, axis positions are in the code’s units, but are converted to the diagnostic’s units defined by the units argument, if provided.

• axis: the name of the requested axis.

• For Field: this is "x", "y" or "z"

• For Probe: this is "axis1", "axis2" or "axis3"

• For ParticleBinning and Screen: this is the type of the axes defined in the namelist

TrackParticles.iterParticles(timestep, chunksize=1)

This method, specific to the tracked particles, provides a fast iterator on chunks of particles for a given timestep. The argument chunksize is the number of particles in each chunk. Note that the data is not ordered by particle ID, meaning that particles are not ordered the same way from one timestep to another.

The returned quantity for each iteration is a python dictionary containing key/value pairs axis:array, where axis is the name of the particle characteristic ("x", "px", etc.) and array contains the corresponding particle values.

Example:

S = happi.Open("path/to/my/results")        # Open the simulation
Diag = S.TrackParticles("my_particles") # Open the tracked particles
npart = 0
sum_px = 0.
# Loop particles of timestep 100 by chunks of 10000
for particle_chunk in Diag.iterParticles(100, chunksize=10000):
npart  += particle_chunk["px"].size
sum_px += particle_chunk["px"].sum()
# Calculate the average px
mean_px = sum_px / npart

Field.getXmoved(timestep)

Specific to Field diagnostics, this method returns the displacement of the moving window at the required timestep.

## Export 2D or 3D data to VTK¶

Field.toVTK(numberOfPieces=1)
Probe.toVTK(numberOfPieces=1)
ParticleBinning.toVTK(numberOfPieces=1)
Performances.toVTK(numberOfPieces=1)
Screen.toVTK(numberOfPieces=1)
TrackParticles.toVTK(rendering='trajectory', data_format='xml')

Converts the data from a diagnostic object to the vtk format. Note the export_dir argument available for each diagnostic (see above).

• numberOfPieces: the number of files into which the data will be split.

• rendering: the type of output in the case of TrackParticles():

• "trajectory": show particle trajectories. One file is generated for all trajectories.

• "cloud": show a cloud of particles. One file is generated for each iteration.

• data_format: the data formatting in the case of TrackParticles(), either "vtk" or "xml". The format "vtk" results in ascii.

Example for tracked particles:

S = happi.Open("path/to/my/results")
tracked_particles = S.TrackParticles("electron", axes=["x","y","z","px","py","pz","Id"], timesteps=[1,10])
# Create cloud of particles in separate files for each iteration
tracked_particles.toVTK(rendering="cloud",data_format="xml");
# Create trajectory in a single file
tracked_particles.toVTK(rendering="trajectory",data_format="xml");


## Plot the data at one timestep¶

This is the first method to plot the data. It produces a static image of the data at one given timestep.

Scalar.plot(...)
Field.plot(...)
Probe.plot(...)
ParticleBinning.plot(...)
TrackParticles.plot(...)
Screen.plot(...)

All these methods have the same arguments described below.

plot(timestep=None, saveAs=None, axes=None, dpi=200, **kwargs)
If the data is 1D, it is plotted as a curve.
If the data is 2D, it is plotted as a map.
If the data is 0D, it is plotted as a curve as function of time.
• timestep: The iteration number at which to plot the data.

• saveAs: name of a directory where to save each frame as figures. You can even specify a filename such as mydir/prefix.png and it will automatically make successive files showing the timestep: mydir/prefix0.png, mydir/prefix1.png, etc.

• axes: Matplotlib’s axes handle on which to plot. If None, make new axes.

• dpi: the number of dots per inch for saveAs.

You may also have keyword-arguments (kwargs) described in Other arguments for diagnostics.

Example:

S = happi.Open("path/to/my/results")
S.ParticleBinning(1).plot(timestep=40, vmin=0, vmax=1e14)


## Plot the data streaked over time¶

This second type of plot works only for 1D data. All available timesteps are streaked to produce a 2D image where the second axis is time.

Scalar.streak(...)
Field.streak(...)
Probe.streak(...)
ParticleBinning.streak(...)
TrackParticles.streak(...)
Screen.streak(...)

All these methods have the same arguments described below.

streak(saveAs=None, axes=None, **kwargs)

All arguments are identical to those of plot, with the exception of timestep.

Example:

S = happi.Open("path/to/my/results")
S.ParticleBinning(1).streak()


## Animated plot¶

This third plotting method animates the data over time.

Scalar.animate(...)
Field.animate(...)
Probe.animate(...)
ParticleBinning.animate(...)
TrackParticles.animate(...)
Screen.animate(...)

All these methods have the same arguments described below.

animate(movie='', fps=15, dpi=200, saveAs=None, axes=None, **kwargs)

All arguments are identical to those of streak, with the addition of:

• movie: name of a file to create a movie, such as "movie.avi" or "movie.gif". If movie="" no movie is created.

• fps: number of frames per second (only if movie requested).

• dpi: number of dots per inch for both movie and saveAs

Example:

S = happi.Open("path/to/my/results")
S.ParticleBinning(1).animate()


## Plot with a slider¶

This methods provides an interactive slider to change the time.

Scalar.slide(...)
Field.slide(...)
Probe.slide(...)
ParticleBinning.slide(...)
TrackParticles.slide(...)
Screen.slide(...)

All these methods have the same arguments described below.

slide(axes=None, **kwargs)

See plot for the description of the arguments.

Example:

S = happi.Open("path/to/my/results")
S.ParticleBinning(1).slide(vmin=0)


## Simultaneous plotting of multiple diagnostics¶

happi.multiPlot(diag1, diag2, ..., **kwargs)

Makes an animated figure containing several plots (one for each diagnostic). If all diagnostics are of similar type, they may be overlayed on only one plot.

• diag1, diag2, etc.
Diagnostics prepared by Scalar(), Field(), Probe(), etc.

Keyword-arguments kwargs are:

• figure: The figure number that is passed to matplotlib (default is 1).

• shape: The arrangement of plots inside the figure. For instance, [2, 1] makes two plots stacked vertically, and [1, 2] makes two plots stacked horizontally. If absent, stacks plots vertically.

• movie : filename to create a movie.

• fps : frames per second for the movie.

• dpi : resolution of the movie or saveAs.

• saveAs: name of a directory where to save each frame as figures. You can even specify a filename such as mydir/prefix.png and it will automatically make successive files showing the timestep: mydir/prefix0.png, mydir/prefix1.png, etc.

• skipAnimation : if True, plots only the last frame.

• timesteps: same as the timesteps argument of the plot() method.

happi.multiSlide(diag1, diag2, ..., **kwargs)

Identical to happi.multiPlot but uses a time slider instead of an animation.

• diag1, diag2, etc.
Diagnostics prepared by Scalar(), Field(), Probe(), etc.
• figure and shape: same as in happi.multiPlot.

Example:

S = happi.Open("path/to/my/results")
A = S.Probe(probeNumber=0, field="Ex")
B = S.ParticleBinning(diagNumber=1)
happi.multiPlot( A, B, figure=1 )


This plots a Probe and a ParticleBinning on the same figure, and makes an animation for all available timesteps.

Note

To plot several quantities on the same graph, you can try shape=[1,1]. One diagnostic may have the option side="right" to use the right-hand-side axis.

In addition to figure, vmin, vmax, xmin, xmax, ymin and ymax, there are many more optional arguments. They are directly passed to the matplotlib package.

For the figure: figsize, dpi, facecolor, edgecolor

For the axes frame: aspect, axis_facecolor, frame_on, position, title, visible, xlabel, xscale, xticklabels, xticks, ylabel, yscale, yticklabels, yticks, zorder

Please refer to matplotlib’s axes options: the same as functions starting with set_ listed here.

For the lines: color, dashes, drawstyle, fillstyle, label, linestyle, linewidth, marker, markeredgecolor, markeredgewidth, markerfacecolor, markerfacecoloralt, markersize, markevery, visible, zorder

Please refer to matplotlib’s line options.

For the image: cmap, aspect, interpolation, norm

For the colorbar: cbaspect, orientation, fraction, pad, shrink, anchor, panchor, extend, extendfrac, extendrect, spacing, ticks, format, drawedges, size, clabel

For the tick number format: style_x, scilimits_x, useOffset_x, style_y, scilimits_y, useOffset_y

For fonts: title_font, xlabel_font, xticklabels_font, ylabel_font, yticklabels_font, colorbar_font

These options are dictionnaries that may contain the entries available in matplotlib’s text options, for instance:

title_font = {'size': 15, 'weight': 'bold', 'family':'serif', 'color': 'k'}


Example:

To choose a gray colormap of the image, use cmap="gray":

S = happi.Open("path/to/my/results")
S.ParticleBinning(0, figure=1, cmap="gray") .plot()


Many colormaps are available from the matplotlib package. With cmap="", you will get a list of available colormaps. Smilei’s default colormaps are: smilei, smilei_r, smileiD and smileiD_r.

## Update the plotting options¶

Scalar.set(...)
Field.set(...)
Probe.set(...)
ParticleBinning.set(...)
Screen.set(...)

Example:

S = happi.Open("path/to/my/results")
A = S.ParticleBinning(diagNumber=0, figure=1, vmax=1)
A.plot( figure=2 )
A.set( vmax=2 )
A.plot()


## Other tools in happi¶

happi.openNamelist(namelist)

Reads a namelist and stores all its content in the returned object.

• namelist: the path to the namelist.

Example:

namelist = happi.openNamelist("path/no/my/namelist.py")
print namelist.Main.timestep