fastpli.simulation.simpli.Simpli

class Simpli(mpi_comm=None)[source]

Bases: fastpli.simulation.__simpli.__Simpli

Simpli Class for simulating 3D-PLI images

Methods

`add_crop_tilt_halo`	add number of necessary boundary voxel from tilted images
`apply_epa`	calculates transmittance, direction and retardation
`apply_optic`	applies optical filters and resampling to image
`apply_optic_resample`	applies optical resampling to image
`apply_rofl`	calculates tilt analysis for direction, inclination and t_rel and additional fitting parameters
`apply_untilt`	applies optical untilt to image with affine transformation
`crop_tilt_pixel`	crop affected boundary pixel from tilted images
`crop_tilt_voxel`	get number of affected boundary voxel from tilted images
`generate_tissue`	Generation of discreticesd tissue.
`get_dict`	Get all member variables which are properties as dictionary
`get_voi`	get volume of interest
`memory_usage`	returns expected memory ussage
`rm_crop_tilt_halo`	remove number of added boundary voxel from tilted images
`run_pipeline`	Automatized hdf5 saving tissue and simulation pipeline
`run_simulation`	Generation of discreticesd tissue.
`run_simulation_pipeline`	Automatized hdf5 saving simulation pipeline
`run_tissue_pipeline`	Automatized hdf5 saving tissue pipeline
`save_parameter_h5`	Saves class members without fiber_bundles in hdf5 file.
`set_dict`	Set dictionary of variables to class members
`set_voi`	set volume of interest

Attributes

`background_absorption`	absorption coefficient of the background
`dim`	(3)-np.ndarray
`dim_origin`	(3)-np.ndarray
`fiber_bundles`	FiberBundles
`filter_rotations`	[float]
`flip_z_beam`	flip light z-direction
`interpolate`	birefringence optical axis interpolation True/False
`light_intensity`	float
`noise_model`	noise model to apply on resampled image
`omp_num_threads`	get/set number of omp threads
`optical_sigma`	optical_sigma of convolution to applie before resampled image
`pixel_size`	side length of pixel of resulting optical image in micro meter
`step_size`	step_size of light inside tissue in voxeln
`tilts`	list of spherical tilting angles [[theta, phi], ...] in radiant:
`tissue_refrection`	tissue refrection to include in tilting light path calculation
`untilt_sensor_view`	automatic untilting of images
`verbose`	bool
`voxel_size`	size length of voxel in micro meter
`wavelength`	wavelength of light

add_crop_tilt_halo()[source]: add number of necessary boundary voxel from tilted images

apply_epa(data, mask=None)

calculates transmittance, direction and retardation

Parameters

data (np.ndarray) – images(x,y,rho)
mask (np.ndarray(bool)) – only True elements will be calculated

Returns

res – transmittance, direction, retardation

Return type

np.ndarray, np.ndarray, np.ndarray

apply_optic(data, mp_pool=None)

applies optical filters and resampling to image

Parameters

data (np.ndarray) – images(x,y,rho)
mp_pool (optional, multiprocessing.Pool)

Returns

res – images in reduced size and applied noise model

Return type

np.ndarray

apply_optic_resample(data, shift=(0, 0), mp_pool=None)

applies optical resampling to image

Parameters

data (np.ndarray) – images(x,y,rho)
mp_pool (optional, multiprocessing.Pool)

Returns

res – images in reduced size

Return type

np.ndarray

apply_rofl(data, mask=None, mp_pool=None, grad_mode=False)

calculates tilt analysis for direction, inclination and t_rel and additional fitting parameters

Parameters

data (np.ndarray) – images(x,y,rho)
mask (optional, np.ndarray(bool)) – only True elements will be calculated
mp_pool (optional, multiprocessing.Pool)
grad_mode (optional, bool) – activate gradient method inside rofl algorithm

Returns

res – (np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray) directionmap, inclmap, trelmap, (dirdevmap, incldevmap, treldevmap, funcmap, itermap)

Return type

np.ndarray, np.ndarray, np.ndarray,

apply_untilt(data, theta, phi, mode='nearest')

applies optical untilt to image with affine transformation

Parameters

data (np.ndarray) – images(x,y,rho)
theta (float) – polar angle
phi (float) – azimuthal angle
mode (str) – method of scipy.interpolate.griddata

Returns

res – untilted images

Return type

np.ndarray

property background_absorption

absorption coefficient of the background

Parameters: background_absorption (float)

crop_tilt_pixel()[source]: crop affected boundary pixel from tilted images

crop_tilt_voxel()[source]: get number of affected boundary voxel from tilted images

property dim

(3)-np.ndarray

Type: dimension of volume in voxel

property dim_origin

(3)-np.ndarray

Type: origin of volume in micro meter

property fiber_bundles

FiberBundles

Type: get fiber_bundles

property filter_rotations

[float]

Type: list of filter rotation position in radiant

property flip_z_beam

flip light z-direction

Parameters: flip_z_beam (bool) – True or False

generate_tissue(only_tissue=False)[source]

Generation of discreticesd tissue.

Parameters: only_tissue (bool) – generates only first return value
Returns: res – discreticed tissue, optical axis and tissue_properties
Return type: np.ndarray, np.ndarray, np.ndarray

get_dict(): Get all member variables which are properties as dictionary

get_voi()

get volume of interest

Returns: res – v_min: [x_min, y_min, z_min] v_max: [x_max, y_max, z_max]
Return type: np.ndarray, np.ndarray

property interpolate

birefringence optical axis interpolation True/False

Returns: res – is the interpolation fo the birefringence optical axis activated
Return type: bool

property light_intensity

float

Type: initial light intensity value in a.u.

memory_usage(unit='MB', item='all')[source]

returns expected memory ussage

Parameters

unit (str) – unit in ‘MB’ or ‘GB’
item (list(str)) – ‘all’ or only ‘tissue’

Returns

res – expected memory ussage

Return type

str

property noise_model

noise model to apply on resampled image

Parameters: fun (function)

property omp_num_threads: get/set number of omp threads

property optical_sigma

optical_sigma of convolution to applie before resampled image

Parameters: optical_sigma (float) – in pixel size

property pixel_size: side length of pixel of resulting optical image in micro meter

rm_crop_tilt_halo(data)[source]: remove number of added boundary voxel from tilted images

run_pipeline(h5f=None, script=None, save=('tissue', 'optical_axis', 'data', 'optic', 'epa', 'mask', 'rofl'), crop_tilt=False, mp_pool=None)[source]

Automatized hdf5 saving tissue and simulation pipeline

Parameters

script (np.ndarray) – save a script in h5f.attrs[‘script’]
save (list(str)) – name of parameters to save
crop_tilt (bool) – crop voxel outside of combined tilting view
mp_pool (multiprocessing.Pool)

Returns

res – (np.ndarray,np.ndarray, np.ndarray) np.ndarray (tissue, optical_axis, tissue_properties), tilting_stack, (rofl_direction, rofl_incl, rofl_t_rel), fom

Return type

(np.ndarray, np.ndarray, np.ndarray), np.ndarray,

run_simulation(tissue, optical_axis, tissue_properties, theta, phi)[source]

Generation of discreticesd tissue.

Parameters

tissue (np.ndarray) – discretized tissue array
optical_axis (np.ndarray) – discretized tissue array
tissue_properties (np.ndarray) – discretized tissue array
theta (float) – polar angle of tilting
phi (float) – azimuthal angle of tilting

Returns

images – images of all rotation angles with a pixel size of voxel_size To calculate the optical resolution and noise apply_optics()

Return type

np.ndarray

run_simulation_pipeline(tissue, optical_axis, tissue_properties, h5f=None, save=('data', 'optic', 'epa', 'mask', 'rofl'), crop_tilt=False, mp_pool=None)[source]

Automatized hdf5 saving simulation pipeline

Parameters

tissue (np.ndarray) – discretized tissue array
optical_axis (np.ndarray) – discretized tissue array
tissue_properties (np.ndarray) – discretized tissue array
h5f (hdf5-File)
save (list(str)) – name of parameters to save
crop_tilt (bool) – crop voxel outside of combined tilting view
mp_pool (multiprocessing.Pool)

Returns

res – discreticed tissue, optical axis and tissue_properties

Return type

np.ndarray, np.ndarray, np.ndarray

run_tissue_pipeline(h5f=None, script=None, save=('tissue', 'optical_axis'))[source]

Automatized hdf5 saving tissue pipeline

Parameters

h5f (hdf5-File)
script (np.ndarray) – save a script in h5f.attrs[‘script’]
save (list(str)) – name of parameters to save

Returns

res – discreticed tissue, optical axis and tissue_properties

Return type

np.ndarray, np.ndarray, np.ndarray

save_parameter_h5(h5f, script=None): Saves class members without fiber_bundles in hdf5 file.

set_dict(data): Set dictionary of variables to class members

set_voi(v_min, v_max)

set volume of interest

Parameters

v_min ((float,float,float)-array-like) – [x_min, y_min, z_min]
v_max ((float,float,float)-array-like) – [x_max, y_max, z_max]

property step_size

step_size of light inside tissue in voxeln

Parameters: step_size (float) – of light inside tissue in voxeln

property tilts

list of spherical tilting angles [[theta, phi], …] in radiant:

Parameters: tilts ([[float, float], …])

property tissue_refrection

tissue refrection to include in tilting light path calculation

Parameters: tissue_refrection (float)

property untilt_sensor_view

automatic untilting of images

Parameters: untilt_sensor_view (bool) – True or False

property verbose

bool

Type: additional information will be printed

property voxel_size: size length of voxel in micro meter

property wavelength

wavelength of light

Parameters: wavelength (float) – in nano meter