# Parameters: Inputs File

This documents on how to use ImpactX with an inputs file (`impactx input_file.in`

).

Note

The AMReX parser (see Math parser and user-defined constants) is used for the right-hand-side of all input parameters that consist of one or more integers or floats, so expressions like `<species_name>.density_max = "2.+1."`

and/or using user-defined constants are accepted.

## Overall simulation parameters

`max_step`

(`integer`

)The number of PIC cycles to perform.

`stop_time`

(`float`

; in seconds)The maximum physical time of the simulation. Can be provided instead of

`max_step`

. If both`max_step`

and`stop_time`

are provided, both criteria are used and the simulation stops when the first criterion is hit.

`amrex.abort_on_out_of_gpu_memory`

(`0`

or`1`

; default is`1`

for true)When running on GPUs, memory that does not fit on the device will be automatically swapped to host memory when this option is set to

`0`

. This will cause severe performance drops. Note that even with this set to`1`

ImpactX will not catch all out-of-memory events yet when operating close to maximum device memory. Please also see the documentation in AMReX.

`amrex.abort_on_unused_inputs`

(`0`

or`1`

; default is`0`

for false)When set to

`1`

, this option causes the simulation to fail*after*its completion if there were unused parameters. It is mainly intended for continuous integration and automated testing to check that all tests and inputs are adapted to API changes.

`impactx.always_warn_immediately`

(`0`

or`1`

; default is`0`

for false)If set to

`1`

, ImpactX immediately prints every warning message as soon as it is generated. It is mainly intended for debug purposes, in case a simulation crashes before a global warning report can be printed.

`impactx.abort_on_warning_threshold`

(string:`low`

,`medium`

or`high`

) optionalOptional threshold to abort as soon as a warning is raised. If the threshold is set, warning messages with priority greater than or equal to the threshold trigger an immediate abort. It is mainly intended for debug purposes, and is best used with

`impactx.always_warn_immediately=1`

. For more information on the warning logger, see this section of the WarpX documentation.

## Setting up the field mesh

`amr.n_cell`

(3 integers) optional (default: 1 blocking_factor per MPI process)The number of grid points along each direction (on the

**coarsest level**)

`amr.max_level`

(`integer`

, default:`0`

)When using mesh refinement, the number of refinement levels that will be used.

Use

`0`

in order to disable mesh refinement.

`amr.ref_ratio`

(`integer`

per refined level, default:`2`

)When using mesh refinement, this is the refinement ratio per level. With this option, all directions are fined by the same ratio.

`amr.ref_ratio_vect`

(3 integers for x,y,z per refined level)When using mesh refinement, this can be used to set the refinement ratio per direction and level, relative to the previous level.

Example: for three levels, a value of

`2 2 4 8 8 16`

refines the first level by 2-fold in x and y and 4-fold in z compared to the coarsest level (level 0/mother grid); compared to the first level, the second level is refined 8-fold in x and y and 16-fold in z.

Note

Field boundaries for space charge calculation are located at the outer ends of the field mesh. We currently assume Dirichlet boundary conditions with zero potential (a mirror charge). Thus, to emulate open boundaries, consider adding enough vacuum padding to the beam. This will be improved in future versions.

Note

Particles that move outside the simulation domain are removed.

`geometry.dynamic_size`

(`boolean`

) optional (default:`true`

for dynamic)Use dynamic (

`true`

) resizing of the field mesh, via`geometry.prob_relative`

, or static sizing (`false`

), via`geometry.prob_lo`

/`geometry.prob_hi`

.

`geometry.prob_relative`

(positive`float`

, unitless) optional (default:`1.0`

)By default, we dynamically extract the minimum and maximum of the particle positions in the beam. The field mesh is expanded, per direction, beyond the physical extent of particles by this factor. For instance,

`0.1`

means 10% more cells above and below the beam for vacuum;`1.0`

means twice as many cells as covered by the beam are used, per direction, for vacuum padding.

`geometry.prob_lo`

and`geometry.prob_hi`

(3 floats, in meters) optional (required if`geometry.dynamic_size`

is`false`

)The extent of the full simulation domain relative to the reference particle position. This can be used to explicitly size the simulation box and ignore

`geometry.prob_relative`

.This box is rectangular, and thus its extent is given here by the coordinates of the lower corner (

`geometry.prob_lo`

) and upper corner (`geometry.prob_hi`

). The first axis of the coordinates is x and the last is z.

## Domain Boundary Conditions

Note

TODO :-)

## Initial Beam Distributions

`<distribution>.type`

(`string`

)Indicates the initial distribution type. This should be one of:

`waterbag`

for initial Waterbag distribution. With additional parameters:`<distribution>.sigmaX`

(`float`

, in meters) rms X`<distribution>.sigmaY`

(`float`

, in meters) rms Y`<distribution>.sigmaT`

(`float`

, in radian) rms normalized time difference T`<distribution>.sigmaPx`

(`float`

, in momentum) rms Px`<distribution>.sigmaPy`

(`float`

, in momentum) rms Py`<distribution>.sigmaPt`

(`float`

, in energy deviation) rms Pt`<distribution>.muxpx`

(`float`

, dimensionless, default:`0`

) correlation X-Px`<distribution>.muypy`

(`float`

, dimensionless, default:`0`

) correlation Y-Py`<distribution>.mutpt`

(`float`

, dimensionless, default:`0`

) correlation T-Pt

`kurth6d`

for initial 6D Kurth distribution. With additional parameters:`<distribution>.sigmaX`

(`float`

, in meters) rms X`<distribution>.sigmaY`

(`float`

, in meters) rms Y`<distribution>.sigmaT`

(`float`

, in radian) rms normalized time difference T`<distribution>.sigmaPx`

(`float`

, in momentum) rms Px`<distribution>.sigmaPy`

(`float`

, in momentum) rms Py`<distribution>.sigmaPt`

(`float`

, in energy deviation) rms Pt`<distribution>.muxpx`

(`float`

, dimensionless, default:`0`

) correlation X-Px`<distribution>.muypy`

(`float`

, dimensionless, default:`0`

) correlation Y-Py`<distribution>.mutpt`

(`float`

, dimensionless, default:`0`

) correlation T-Pt

`gaussian`

for initial 6D Gaussian (normal) distribution. With additional parameters:`<distribution>.sigmaX`

(`float`

, in meters) rms X`<distribution>.sigmaY`

(`float`

, in meters) rms Y`<distribution>.sigmaT`

(`float`

, in radian) rms normalized time difference T`<distribution>.sigmaPx`

(`float`

, in momentum) rms Px`<distribution>.sigmaPy`

(`float`

, in momentum) rms Py`<distribution>.sigmaPt`

(`float`

, in energy deviation) rms Pt`<distribution>.muxpx`

(`float`

, dimensionless, default:`0`

) correlation X-Px`<distribution>.muypy`

(`float`

, dimensionless, default:`0`

) correlation Y-Py`<distribution>.mutpt`

(`float`

, dimensionless, default:`0`

) correlation T-Pt

`kvdist`

for initial K-V distribution in the transverse plane. The distribution is uniform in t and Gaussian in pt. With additional parameters:`<distribution>.sigmaX`

(`float`

, in meters) rms X`<distribution>.sigmaY`

(`float`

, in meters) rms Y`<distribution>.sigmaT`

(`float`

, in radian) rms normalized time difference T`<distribution>.sigmaPx`

(`float`

, in momentum) rms Px`<distribution>.sigmaPy`

(`float`

, in momentum) rms Py`<distribution>.sigmaPt`

(`float`

, in energy deviation) rms Pt`<distribution>.muxpx`

(`float`

, dimensionless, default:`0`

) correlation X-Px`<distribution>.muypy`

(`float`

, dimensionless, default:`0`

) correlation Y-Py`<distribution>.mutpt`

(`float`

, dimensionless, default:`0`

) correlation T-Pt

`kurth4d`

for initial 4D Kurth distribution in the transverse plane. The distribution is uniform in t and Gaussian in pt. With additional parameters:`<distribution>.sigmaX`

(`float`

, in meters) rms X`<distribution>.sigmaY`

(`float`

, in meters) rms Y`<distribution>.sigmaT`

(`float`

, in radian) rms normalized time difference T`<distribution>.sigmaPx`

(`float`

, in momentum) rms Px`<distribution>.sigmaPy`

(`float`

, in momentum) rms Py`<distribution>.sigmaPt`

(`float`

, in energy deviation) rms Pt`<distribution>.muxpx`

(`float`

, dimensionless, default:`0`

) correlation X-Px`<distribution>.muypy`

(`float`

, dimensionless, default:`0`

) correlation Y-Py`<distribution>.mutpt`

(`float`

, dimensionless, default:`0`

) correlation T-Pt

`semigaussian`

for initial Semi-Gaussian distribution. The distribution is uniform within a cylinder in (x,y,z) and Gaussian in momenta (px,py,pt). With additional parameters:`<distribution>.sigmaX`

(`float`

, in meters) rms X`<distribution>.sigmaY`

(`float`

, in meters) rms Y`<distribution>.sigmaT`

(`float`

, in radian) rms normalized time difference T`<distribution>.sigmaPx`

(`float`

, in momentum) rms Px`<distribution>.sigmaPy`

(`float`

, in momentum) rms Py`<distribution>.sigmaPt`

(`float`

, in energy deviation) rms Pt`<distribution>.muxpx`

(`float`

, dimensionless, default:`0`

) correlation X-Px`<distribution>.muypy`

(`float`

, dimensionless, default:`0`

) correlation Y-Py`<distribution>.mutpt`

(`float`

, dimensionless, default:`0`

) correlation T-Pt

## Lattice Elements

`lattice.elements`

(`list of strings`

) optional (default: no elements)A list of names (one name per lattice element), in the order that they appear in the lattice.

`lattice.nslice`

(`integer`

) optional (default:`1`

)A positive integer specifying the number of slices used for the application of space charge in all elements; overwritten by element parameter “nslice”

`<element_name>.type`

(`string`

)Indicates the element type for this lattice element. This should be one of:

`drift`

for free drift. This requires these additional parameters:`<element_name>.ds`

(`float`

, in meters) the segment length`<element_name>.nslice`

(`integer`

) number of slices used for the application of space charge (default:`1`

)

`quad`

for a quadrupole. This requires these additional parameters:`<element_name>.ds`

(`float`

, in meters) the segment length`<element_name>.k`

(`float`

, in inverse meters squared) the quadrupole strength= (magnetic field gradient in T/m) / (magnetic rigidity in T-m)

k > 0 horizontal focusing

k < 0 horizontal defocusing

`<element_name>.nslice`

(`integer`

) number of slices used for the application of space charge (default:`1`

)

`quadrupole_softedge`

for a soft-edge quadrupole. This requires these additional parameters:`<element_name>.ds`

(`float`

, in meters) the segment length`<element_name>.gscale`

(`float`

, in inverse meters) Scaling factor for on-axis magnetic field gradient`<element_name>.cos_coefficients`

(array of`float`

) cos coefficients in Fourier expansion of the on-axis field gradient (optional); default is a tanh fringe field model from MaryLie 3.0`<element_name>.sin_coefficients`

(array of`float`

) sin coefficients in Fourier expansion of the on-axis field gradient (optional); default is a tanh fringe field model from MaryLie 3.0`<element_name>.mapsteps`

(`integer`

) number of integration steps per slice used for map and reference particle push in applied fields(default:

`1`

)

`<element_name>.nslice`

(`integer`

) number of slices used for the application of space charge (default:`1`

)

`sbend`

for a bending magnet. This requires these additional parameters:`<element_name>.ds`

(`float`

, in meters) the segment length`<element_name>.rc`

(`float`

, in meters) the bend radius`<element_name>.nslice`

(`integer`

) number of slices used for the application of space charge (default:`1`

)

`solenoid`

for an ideal hard-edge solenoid magnet. This requires these additional parameters:`<element_name>.ds`

(`float`

, in meters) the segment length`<element_name>.ks`

(`float`

, in meters) Solenoid strength in m^(-1) (MADX convention)= (magnetic field Bz in T) / (rigidity in T-m)

`<element_name>.nslice`

(`integer`

) number of slices used for the application of space charge (default:`1`

)

`solenoid_softedge`

for a soft-edge solenoid. This requires these additional parameters:`<element_name>.ds`

(`float`

, in meters) the segment length`<element_name>.bscale`

(`float`

, in inverse meters) Scaling factor for on-axis magnetic field Bz`<element_name>.cos_coefficients`

(array of`float`

) cos coefficients in Fourier expansion of the on-axis magnetic field Bz (optional); default is a thin-shell model from DOI:10.1016/J.NIMA.2022.166706`<element_name>.sin_coefficients`

(array of`float`

) sin coefficients in Fourier expansion of the on-axis magnetic field Bz (optional); default is a thin-shell model from DOI:10.1016/J.NIMA.2022.166706`<element_name>.mapsteps`

(`integer`

) number of integration steps per slice used for map and reference particle push in applied fields (default:`1`

)`<element_name>.nslice`

(`integer`

) number of slices used for the application of space charge (default:`1`

)

`dipedge`

for dipole edge focusing. This requires these additional parameters:`<element_name>.psi`

(`float`

, in radians) the pole face rotation angle`<element_name>.rc`

(`float`

, in meters) the bend radius`<element_name>.g`

(`float`

, in meters) the gap size`<element_name>.K2`

(`float`

, dimensionless) normalized field integral for fringe field

`constf`

for a constant focusing element. This requires these additional parameters:`<element_name>.ds`

(`float`

, in meters) the segment length`<element_name>.kx`

(`float`

, in 1/meters) the horizontal focusing strength`<element_name>.ky`

(`float`

, in 1/meters) the vertical focusing strength`<element_name>.kt`

(`float`

, in 1/meters) the longitudinal focusing strength`<element_name>.nslice`

(`integer`

) number of slices used for the application of space charge (default:`1`

)

`rfcavity`

a radiofrequency cavity. This requires these additional parameters:`<element_name>.ds`

(`float`

, in meters) the segment length`<element_name>.escale`

(`float`

, in 1/m) scaling factor for on-axis RF electric field= (peak on-axis electric field Ez in MV/m) / (particle rest energy in MeV)

`<element_name>.freq`

(`float`

, in Hz) RF frequency`<element_name>.phase`

(`float`

, in degrees) RF driven phase`<element_name>.cos_coefficients`

(array of`float`

) cosine coefficients in Fourier expansion of on-axis electric field Ez (optional); default is a 9-cell TESLA superconducting cavity model from DOI:10.1103/PhysRevSTAB.3.092001`<element_name>.cos_coefficients`

(array of`float`

) sine coefficients in Fourier expansion of on-axis electric field Ez (optional); default is a 9-cell TESLA superconducting cavity model from DOI:10.1103/PhysRevSTAB.3.092001`<element_name>.mapsteps`

(`integer`

) number of integration steps per slice used for map and reference particle push in applied fields (default:`1`

)`<element_name>.nslice`

(`integer`

) number of slices used for the application of space charge (default:`1`

)

`shortrf`

for a short RF (bunching) cavity element. This requires these additional parameters:`<element_name>.V`

(`float`

, dimensionless) normalized voltage drop across the cavity= (maximum voltage drop in Volts) / (speed of light in m/s * magnetic rigidity in T-m)

`<element_name>.k`

(`float`

, in 1/meters) the RF wavenumber= 2*pi/(RF wavelength in m)

`multipole`

for a thin multipole element. This requires these additional parameters:`<element_name>.multipole`

(`integer`

, dimensionless) order of multipole(m = 1) dipole, (m = 2) quadrupole, (m = 3) sextupole, etc.

`<element_name>.k_normal`

(`float`

, in 1/meters^m) integrated normal multipole coefficient (MAD-X convention)= 1/(magnetic rigidity in T-m) * (derivative of order m-1 of By with respect to x)

`<element_name>.k_skew`

(`float`

, in 1/meters^m) integrated skew multipole strength (MAD-X convention)

`nonlinear_lens`

for a thin IOTA nonlinear lens element. This requires these additional parameters:`<element_name>.knll`

(`float`

, in meters) integrated strength of the lens segment (MAD-X convention)= dimensionless lens strength * c parameter**2 * length / Twiss beta

`<element_name>.cnll`

(`float`

, in meters) distance of the singularities from the origin (MAD-X convention)= c parameter * sqrt(Twiss beta)

`beam_monitor`

a beam monitor, writing all beam particles at fixed`s`

to openPMD files. If the same element name is used multiple times, then an output series is created with multiple outputs.`<element_name>.name`

(`string`

, default value:`<element_name>`

)The output series name to use. By default, output is created under

`diags/openPMD/<element_name>.<backend>`

.`<element_name>.backend`

(`string`

, default value:`default`

)I/O backend for openPMD data dumps.

`bp`

is the ADIOS2 I/O library,`h5`

is the HDF5 format, and`json`

is a simple text format.`json`

only works with serial/single-rank jobs. By default, the first available backend in the order given above is taken.`<element_name>.encoding`

(`string`

, default value:`g`

)openPMD iteration encoding: (v)ariable based, (f)ile based, (g)roup based (default) variable based is an experimental feature with ADIOS2.

## Distribution across MPI ranks and parallelization

`amr.max_grid_size`

(`integer`

) optional (default:`128`

)Maximum allowable size of each

**subdomain**(expressed in number of grid points, in each direction). Each subdomain has its own ghost cells, and can be handled by a different MPI rank ; several OpenMP threads can work simultaneously on the same subdomain.If

`max_grid_size`

is such that the total number of subdomains is**larger**that the number of MPI ranks used, than some MPI ranks will handle several subdomains, thereby providing additional flexibility for**load balancing**.When using mesh refinement, this number applies to the subdomains of the coarsest level, but also to any of the finer level.

## Math parser and user-defined constants

ImpactX uses AMReX’s math parser that reads expressions in the input file. It can be used in all input parameters that consist of one or more integers or floats. Integer input expecting boolean, 0 or 1, are not parsed. Note that when multiple values are expected, the expressions are space delimited. For integer input values, the expressions are evaluated as real numbers and the final result rounded to the nearest integer. See this section of the AMReX documentation for a complete list of functions supported by the math parser.

### ImpactX constants

ImpactX will provide a few pre-defined constants, that can be used for any parameter that consists of one or more floats.

Note

Develop, such as:

q_e |
elementary charge |

m_e |
electron mass |

m_p |
proton mass |

m_u |
unified atomic mass unit (Dalton) |

epsilon0 |
vacuum permittivity |

mu0 |
vacuum permeability |

clight |
speed of light |

pi |
math constant pi |

See in WarpX the file `Source/Utils/WarpXConst.H`

for the values.

### User-defined constants

Users can define their own constants in the input file.
These constants can be used for any parameter that consists of one or more integers or floats.
User-defined constant names can contain only letters, numbers and the character `_`

.
The name of each constant has to begin with a letter. The following names are used
by ImpactX, and cannot be used as user-defined constants: `x`

, `y`

, `z`

, `X`

, `Y`

, `t`

.
The values of the constants can include the predefined ImpactX constants listed above as well as other user-defined constants.
For example:

`my_constants.a0 = 3.0`

`my_constants.z_plateau = 150.e-6`

`my_constants.n0 = 1.e22`

`my_constants.wp = sqrt(n0*q_e**2/(epsilon0*m_e))`

### Coordinates

Besides, for profiles that depend on spatial coordinates (the plasma momentum distribution or the laser field, see below `Particle initialization`

and `Laser initialization`

), the parser will interpret some variables as spatial coordinates.
These are specified in the input parameter, i.e., `density_function(x,y,z)`

and `field_function(X,Y,t)`

.

The parser reads python-style expressions between double quotes, for instance
`"a0*x**2 * (1-y*1.e2) * (x>0)"`

is a valid expression where `a0`

is a
user-defined constant (see above) and `x`

and `y`

are spatial coordinates. The names are case sensitive. The factor
`(x>0)`

is `1`

where `x>0`

and `0`

where `x<=0`

. It allows the user to
define functions by intervals.
Alternatively the expression above can be written as `if(x>0, a0*x**2 * (1-y*1.e2), 0)`

.

## Numerics and algorithms

`algo.particle_shape`

(`integer`

;`1`

,`2`

, or`3`

)The order of the shape factors (splines) for the macro-particles along all spatial directions: 1 for linear, 2 for quadratic, 3 for cubic. Low-order shape factors result in faster simulations, but may lead to more noisy results. High-order shape factors are computationally more expensive, but may increase the overall accuracy of the results. For production runs it is generally safer to use high-order shape factors, such as cubic order.

`algo.space_charge`

(`boolean`

, optional, default:`true`

)Whether to calculate space charge effects. This is in-development. At the moment, this flag only activates coordinate transformations and charge deposition.

## Diagnostics and output

`diag.enable`

(`boolean`

, optional, default:`true`

) Enable or disable diagnostics generally. Disabling this is mostly used for benchmarking.This option is ignored for the openPMD output elements (remove them from the lattice to disable).

`diag.slice_step_diagnostics`

(`boolean`

, optional, default:`false`

) By default, diagnostics is performed at the beginning and end of the simulation. Enabling this flag will write diagnostics every step and slice step`diag.file_min_digits`

(`integer`

, optional, default:`6`

)The minimum number of digits used for the step number appended to the diagnostic file names.

### Reduced Diagnostics

Reduced diagnostics allow the user to compute some reduced quantity (invariants of motion, particle temperature, max of a field, …) and write a small amount of data to text files.
Reduced diagnostics are run *in situ* with the simulation.

Diagnostics related to integrable optics in the IOTA nonlinear magnetic insert element:

`diag.alpha`

(`float`

, unitless) Twiss alpha of the bare linear lattice at the location of output for the nonlinear IOTA invariants H and I. Horizontal and vertical values must be equal.`diag.beta`

(`float`

, meters) Twiss beta of the bare linear lattice at the location of output for the nonlinear IOTA invariants H and I. Horizontal and vertical values must be equal.`diag.tn`

(`float`

, unitless) dimensionless strength of the IOTA nonlinear magnetic insert element used for computing H and I.`diag.cn`

(`float`

, meters^(1/2)) scale factor of the IOTA nonlinear magnetic insert element used for computing H and I.

## Checkpoints and restart

Note

ImpactX will support checkpoints/restart via AMReX.
The checkpoint capability can be turned with regular diagnostics: `<diag_name>.format = checkpoint`

.

`amr.restart`

(string)Name of the checkpoint file to restart from. Returns an error if the folder does not exist or if it is not properly formatted.

## Intervals parser

Note

TODO :-)

ImpactX can parse time step interval expressions of the form `start:stop:period`

, e.g.
`1:2:3, 4::, 5:6, :, ::10`

.
A comma is used as a separator between groups of intervals, which we call slices.
The resulting time steps are the union set of all given slices.
White spaces are ignored.
A single slice can have 0, 1 or 2 colons `:`

, just as numpy slices, but with inclusive upper bound for `stop`

.

For 0 colon the given value is the period

For 1 colon the given string is of the type

`start:stop`

For 2 colons the given string is of the type

`start:stop:period`

Any value that is not given is set to default.
Default is `0`

for the start, `std::numeric_limits<int>::max()`

for the stop and `1`

for the
period.
For the 1 and 2 colon syntax, actually having values in the string is optional
(this means that `::5`

, `100 ::10`

and `100 :`

are all valid syntaxes).

All values can be expressions that will be parsed in the same way as other integer input parameters.

**Examples**

`something_intervals = 50`

-> do something at timesteps 0, 50, 100, 150, etc. (equivalent to`something_intervals = ::50`

)`something_intervals = 300:600:100`

-> do something at timesteps 300, 400, 500 and 600.`something_intervals = 300::50`

-> do something at timesteps 300, 350, 400, 450, etc.`something_intervals = 105:108,205:208`

-> do something at timesteps 105, 106, 107, 108, 205, 206, 207 and 208. (equivalent to`something_intervals = 105 : 108 : , 205 : 208 :`

)`something_intervals = :`

or`something_intervals = ::`

-> do something at every timestep.`something_intervals = 167:167,253:253,275:425:50`

do something at timesteps 167, 253, 275, 325, 375 and 425.

This is essentially the python slicing syntax except that the stop is inclusive
(`0:100`

contains 100) and that no colon means that the given value is the period.

Note that if a given period is zero or negative, the corresponding slice is disregarded.
For example, `something_intervals = -1`

deactivates `something`

and
`something_intervals = ::-1,100:1000:25`

is equivalent to `something_intervals = 100:1000:25`

.