febid.monte_carlo.etrajectory.ETrajectory#

class ETrajectory[source]#

Bases: MC_Sim_Base

A class responsible for the generation and scattering of electron trajectories

Methods

`get_crossing_point`
`get_next_crossing`	Get next two crossing points and a flag showing if volume boundaries are met
`get_norm_factor`	Calculate norming factor with the given number of generated trajectories
`map_trajectory`	Simulate trajectory of the electrons with a specified starting position.
`map_trajectory_verbose`	Simulate trajectory of the electrons with a specified starting position.
`map_wrapper`	Create normally distributed electron positions and run trajectory mapping
`map_wrapper_cy`	Create normally distributed electron positions and run trajectory mapping in Cython
`plot_distribution`	Plot a scatter plot of the (x,y) points with 2D histograms depicting axial distribution
`rnd_gauss_xy`	Generate a specified number of points according to a Gaussian distribution.
`rnd_super_gauss`	Generate a specified number of points according to a Super Gaussian distribution.
`save_passes`	Save passes to a text file or by pickling
`setParameters`	Initialise the instance and set all the necessary parameters

Attributes

`NA`
`elementary_charge`
`shape`
`shape_abs`

get_next_crossing(coords)[source]#

Get next two crossing points and a flag showing if volume boundaries are met

Parameters:: coords –
Returns:

get_norm_factor(N=None)[source]#

Calculate norming factor with the given number of generated trajectories

Parameters:: N – number of trajectories
Returns:

map_trajectory(x0, y0)[source]#

Simulate trajectory of the electrons with a specified starting position.

Parameters:

x0 – x-positions of the electrons
y0 – y-positions of the electrons

Returns:

map_trajectory_verbose(x0, y0)[source]#

Simulate trajectory of the electrons with a specified starting position. Version with step-by-step output to console.

Parameters:

x0 – x-positions of the electrons
y0 – y-positions of the electrons

Returns:

map_wrapper(y0, x0, N=0)[source]#

Create normally distributed electron positions and run trajectory mapping

Parameters:

y0 – y-position of the beam, nm
x0 – x-position of the beam, nm
N – number of electrons to create

Returns:

map_wrapper_cy(y0, x0, N=0)[source]#

Create normally distributed electron positions and run trajectory mapping in Cython

Parameters:

y0 – y-position of the beam, nm
x0 – x-position of the beam, nm
N – number of electrons to create

Returns:

plot_distribution(x, y, func=None)[source]#

Plot a scatter plot of the (x,y) points with 2D histograms depicting axial distribution

Parameters:

x – array of x-coordinates
y – array of y-coordinates
func – 2D probability density function

Returns:

rnd_gauss_xy(x0, y0, N)[source]#

Generate a specified number of points according to a Gaussian distribution. Standard deviation and order of the super gaussian are class properties.

Parameters:

x0 – mean along X-axis
y0 – mean along Y-axis
N – number of points to generate

Returns:

two arrays of N-length with x and y positions

rnd_super_gauss(x0, y0, N)[source]#

Generate a specified number of points according to a Super Gaussian distribution. Standard deviation and order of the super gaussian are class properties.

Parameters:

x0 – mean along X-axis
y0 – mean along Y-axis
N – number of points to generate

Returns:

two arrays of N-length with x and y positions

save_passes(fname, type)[source]#

Save passes to a text file or by pickling

Parameters:

fname – name of the file
type – saving type: accepts ‘pickle’ or ‘text’

Returns:

setParameters(structure, params, stat=1000)[source]#

Initialise the instance and set all the necessary parameters

Parameters:

structure – solid structure representation
params – contains all input parameters for the simulation
stat – number of simulated trajectories