Visualization, tools¶

visu¶

visu contains 2D visualization functions.

structure geometry¶

structure¶

pyGDM2.visu.structure(struct, projection='XY', color='auto', scale=1, borders=50, marker='s', EACHN=1, tit='', plot_legend=True, ax=None, absscale=False, show=True, **kwargs)¶

plot structure in 2d, projected to e.g. the XY plane

plot the structure struct as a scatter projection to a carthesian plane. Either from list of coordinates, or using a simulation definition as input.

kwargs are passed to matplotlib’s scatter

Parameters:	struct (list or `core.simulation`) – either list of 3d coordinate tuples or simulation description object projection (str, default: "XY") – which 2D-projection to plot. One of “XY”, “YZ”, “XZ” color (str or matplotlib color, default: "auto") – Color of scatterplot. Either “auto”, or matplotlib-compatible color. “auto”: automatic color selection (multi-color for multiple materials). scale (float, default: 0.5) – symbol scaling in units of stepsize borders (float, default: 50) – additional space limits around plot in nm marker (str, default "s" (=squares)) – scatter symbols for plotting meshpoints. Convention of matplotlib’s scatter EACHN (int, default: 1 (=all)) – show each EACHN points only tit (str, default: "") – title for plot (optional) plot_legend (bool, default: True) – whether to add a legend if multi-material structure (requires auto-color enabled) ax (matplotlib axes, default: None (=create new)) – axes object (matplotlib) to plot into absscale (bool, default: False) – absolute or relative scaling. If True, override internal scaling calculation show (bool, default: True) – directly show plot
Returns:
Return type:	result returned by matplotlib’s scatter

structure_contour¶

pyGDM2.visu.structure_contour(struct, projection='XY', color='b', N_points=4, borders=50, lw=1, s=5, style='lines', input_mesh='cube', tit='', add_first_point=True, show=True, **kwargs)¶

Contour around structure

further kwargs are passed to matplotlib’s scatter or plot

Parameters:	struct (list or `core.simulation`) – either list of 3d coordinate tuples or simulation description object projection (str, default: "XY") – which 2D-projection to plot. One of “XY”, “YZ”, “XZ” color (str or matplotlib color, default: "b") – matplotlib-compatible color for scatter N_points (int, default: 4) – number of additional points to calculate at each meshpoint-side (basically meant for style “dots”, default: 4) borders (float, default: 50) – additional space limits around plot in nm lw (float, default: 1) – linewidth as used by pyplot.plot (style=’lines’ only) s (float, default: 5) – symbol scaling for scatterplot as used by pyplot.scatter (style=’dots’ only) stlye (str, default: 'lines') – Style of plot. One of [“dots”, “lines”] input_mesh (str, default: 'cube') – mesh type, default: “cube”. one of [“cube”, “hex”, “hex2”, “hex_onelayer”] tit (str, default: "") – title for plot (optional) add_first_point (bool, default: True) – If True, adds first point twice in order to close the contour. Might cause some strange lines if a single edge meshpoint is first in the geometry. show (bool, default: True) – directly show plot
Returns:	contour plot object
Return type:	matplotlib’s scatter or line return value

vector fields¶

vectorfield¶

pyGDM2.visu.vectorfield(NF, struct=None, projection='XY', complex_part='real', tit='', slice_level=None, scale=10.0, vecwidth=1.0, cmap=<matplotlib.colors.LinearSegmentedColormap object>, cmin=0.3, ax=None, show=True, borders=50, EACHN=1, sortL=True, overrideMaxL=False, **kwargs)¶

plot 2D Vector field as quiver plot

plot nearfield list as 2D vector plot, using matplotlib’s quiver. kwargs are passed to pyplot.quiver

Parameters:	NF (list of 3- or 6-tuples) – Nearfield definition. np.array, containing 6-tuples: (X,Y,Z, Ex,Ey,Ez), the field components being complex (use e.g. `tools.get_field_as_list()`). Optionally can also take a list of 3-tuples (Ex, Ey, Ez), in which case the structure must be provided via the struct kwarg. struct (list or `core.simulation`, optional) – optional structure definition (necessary if field is supplied in 3-tuple form without coordinates). Either simulation description object, or list of (x,y,z) coordinate tuples projection (str, default: 'XY') – Which projection to plot: “XY”, “YZ”, “XZ” complex_part (str, default: 'real') – Which part of complex field to plot. Either ‘real’ or ‘imag’. tit (str, default: "") – title for plot (optional) slice_level (float, default: None) – optional value of depth where to slice. eg if projection==’XY’, slice_level=10 will take only values where Z==10. slice_level = None, plot all vectors one above another without slicing. slice_level = -9999 : take minimum value in field-list. slice_level = 9999 : take maximum value in field-list. scale (float, default: 10.0) – optional vector length scaling parameter vecwidth (float, default: 1.0) – optional vector width scaling parameter cmap (matplotlib colormap, default: cm.Blues) – matplotlib colormap to use for arrows (color scaling by vector length) cmin (float, default: 0.3) – minimal color to use from cmap to avoid pure white ax (matplotlib axes, default: None (=create new)) – optinal axes object (mpl) to plot into show (bool, default: True) – whether or not to call pyplot.show borders (float, default: 50) – additional space in nm to plotting borders EACHN (int, default: 1 [=all]) – show each N points only sortL (bool, default: True) – sort vectors by length to avoid clipping (True: Plot longest vectors on top)
Returns:
Return type:	return value of matplotlib’s quiver

vectorfield_by_fieldindex¶

pyGDM2.visu.vectorfield_by_fieldindex(sim, field_index, **kwargs)¶

Wrapper to vectorfield(), using simulation object and fieldindex as input

All other keyword arguments are passed to vectorfield().

Parameters:	sim (simulation) – instance of `core.simulation` field_index (int) – index of evaluated self-consistent field to use for calculation. Can be obtained for specific parameter-set using `tools.get_closest_field_index()`

vectorfield_fieldlines¶

pyGDM2.visu.vectorfield_fieldlines(NF, projection='XY', tit='', complex_part='real', borders=0, NX=-1, NY=-1, show=True, **kwargs)¶

2d plot of fieldlines of field ‘NF’

other optional arguments passed to matplotlib’s pyplot.streamplot

Parameters:	NF (list of 6-tuples) – Nearfield definition. np.array, containing 6-tuples: (X,Y,Z, Ex,Ey,Ez), the field components being complex (use e.g. `tools.get_field_as_list()`). projection (str, default: 'XY') – Which projection to plot: “XY”, “YZ”, “XZ” complex_part (str, default: 'real') – Which part of complex field to plot. Either ‘real’ or ‘imag’. tit (str, default: "") – title for plot (optional) borders (float, default: 50) – additional space in nm to plotting borders NY (NX,) – optional interpolation steps for nearfield. by default take number of independent positions in NF (if NX or NY == -1) show (bool, default: True) – whether or not to call pyplot.show
Returns:	return matplotlib streamplot object
Return type:	if show == False

scalar fields¶

vectorfield_color¶

pyGDM2.visu.vectorfield_color(NF, projection='XY', complex_part='real', tit='', slice_level=-9999, NX=None, NY=None, fieldComp='I', borders=0, cmap='seismic', clim=None, show=True, **kwargs)¶

plot of 2D field data as colorplot

other kwargs are passed to matplotlib’s imshow

Parameters:	NF (list of 6-tuples) – Nearfield definition. np.array, containing 6-tuples: (X,Y,Z, Ex,Ey,Ez), the field components being complex (use e.g. `tools.get_field_as_list()`). projection (str, default: 'XY') – Which projection to plot: “XY”, “YZ”, “XZ” complex_part (str, default: 'real') – Which part of complex field to plot. Either ‘real’ or ‘imag’. tit (str, default: "") – title for plot (optional) slice_level (float, default: None) – optional value of depth where to slice. eg if projection==’XY’, slice_level=10 will take only values where Z==10. slice_level = None, plot all vectors one above another without slicing. slice_level = -9999 : take minimum value in field-list. slice_level = 9999 : take maximum value in field-list. NY (NX,) – optional interpolation steps for nearfield. by default take number of independent positions in NF (if NX or NY == -1) fieldComp (str, default: 'I') – Which component to use. One of [“I”, “Ex”, “Ey”, “Ez”]. If “I” is used, complex_part argument has no effect. borders (float, default: 50) – additional space in nm to plotting borders cmap (matplotlib colormap, default: "seismic") – matplotlib colormap to use for colorplot clim (float, default: None) – optional colormap limits to pass to plt.clim() show (bool, default: True) – whether or not to call pyplot.show
Returns:
Return type:	result of matplotlib’s imshow

vectorfield_color_by_fieldindex¶

pyGDM2.visu.vectorfield_color_by_fieldindex(sim, field_index, **kwargs)¶

Wrapper to vectorfield_color(), using simulation object and fieldindex as input

All other keyword arguments are passed to vectorfield_color().

Parameters:	sim (simulation) – instance of `core.simulation` field_index (int) – index of evaluated self-consistent field to use for calculation. Can be obtained for specific parameter-set using `tools.get_closest_field_index()`

scalarfield¶

pyGDM2.visu.scalarfield(NF, **kwargs)¶

Wrapper to vectorfield_color(), using scalar data tuples (x,y,z,S) as input

All other keyword arguments are passed to vectorfield_color().

Parameters:	NF (list of 4-tuples) – list of tuples (x,y,z,S). Alternatively, the scalar-field can be passed as list of 2 lists containing the x/y positions and scalar values, respectively. ([xy-values, S], with xy-values: list of 2-tuples [x,y]; S: list of scalars). This format is returned e.g. by `tools.calculate_rasterscan()`.

farfield_pattern_2D¶

pyGDM2.visu.farfield_pattern_2D(theta, phi, I, degrees=True, show=True, **kwargs)¶

Plot BFP-like 2D far-field radiation pattern

Plot a “back-focal plane”-like radiation pattern. All arrays are of shape (Nteta, Nphi)

kwargs are passed to matplotlib’s pyplot.pcolormesh.

Parameters:	tetalist (2D-numpy.ndarray, float) – teta angles philist (2D-numpy.ndarray, float) – phi angles Iff (2D-numpy.ndarray, float) – Intensities at (teta, phi) positions degrees (bool, default: True) – Transform polar angle to degrees for plotting (False: radians) show (bool, default: True) – whether to call pyplot.show()
Returns:
Return type:	result of matplotlib’s pyplot.pcolormesh

animation¶

animate_vectorfield¶

pyGDM2.visu.animate_vectorfield(NF, Nframes=50, projection='XY', doQuiver=True, doColor=False, alphaColor=1.0, scale=5.0, clim=None, kwargs={'cmin': 0.3}, kwargsColor={'fieldComp': 'Ex'}, ax=None, show=True)¶

Create animation of oszillating complex nearfield (2D-Projection)

Parameters:	NF (list of 6-tuples) – Nearfield definition. np.array, containing 6-tuples: (X,Y,Z, Ex,Ey,Ez), the field components being complex (use e.g. `tools.get_field_as_list()`). Nframes (int, default: 50) – number of frames per oscillation cycle projection (str, default: 'XY') – 2D projection plane doQuiver (bool, default: True) – plot field as quiverplot. doColor (bool, default: True) – plot field as colorplot (real part) alphaColor (float, default: 0.5) – alpha value of colormap kwargs (dict, default: {}) – are passed to `vectorfield()` (if doQuiver) kwargsColor (dict, default: {}) – are passed to `vectorfield_color()` (if doColor) ax (optional matplotlib axes, default: None) – optional axes object (mpl) to plot into show (bool, default: True) – directly show animation
Returns:	im_ani
Return type:	Animation as matplotlib “ArtistAnimation” object

Notes

You can save the animation as video-file using: im_ani.save(‘video.mp4’, writer=”ffmpeg”, codec=’h264’, bitrate=1500). See also matplotlib’s documentation of the animation module for more info.

visu3d¶

visu3d contains 3D visualization functions, the API is compatible to the visu module.

structure geometry¶

structure¶

pyGDM2.visu3d.structure(struct, scale=0.75, abs_scale=False, tit='', color=(0.3, 0.3, 0.3), mode='cube', draw_substrate=True, substrate_size=2.0, axis_labels=True, show=True, **kwargs)¶

plot structure in 3d

plot the structure “struct” using 3d points. Either from list of coordinates, or using a simulation definition dict as input.

Parameters:	struct (-) – scale (-) – abs_scale (-) – color (-) – mode (-) – draw_substrate (-) – substrate_size (-) – axis_labels (-) – show (-) – kwargs (-) –

vector fields¶

vectorfield¶

pyGDM2.visu3d.vectorfield(NF, struct=None, scale=1.5, abs_scale=False, tit='', complex_part='real', axis_labels=True, show=True, **kwargs)¶

3d quiverplot of nearfield

Parameters:	NF (-) – (X,Y,Z, Ex,Ey,Ez), the field components being complex. struct (-) – form without coordinates). Either simulation object, or list of coordinate (x,y,z) tuples scale (-) – abs_scale (-) – complex_part (-) – Either ‘real’ or ‘imag’. (default: ‘real’) axis_labels (-) – show (-) –

All other keyword arguments are passed to mlab’s quiver3d.

vectorfield_by_fieldindex¶

pyGDM2.visu3d.vectorfield_by_fieldindex(sim, field_index, **kwargs)¶

Wrapper to vectorfield(), using simulation object and fieldindex as input

Parameters:	sim (simulation) – instance of `core.simulation` field_index (int) – index of evaluated self-consistent field to use for calculation. Can be obtained for specific parameter-set using `tools.get_closest_field_index()`

:param All other keyword arguments are passed to vectorfield().:

scalar fields¶

vectorfield_color¶

pyGDM2.visu3d.vectorfield_color(NF, complex_part='real', fieldComp='I', scale=0.5, abs_scale=False, mode='sphere', tit='', axis_labels=True, show=True, **kwargs)¶

plot of scalar electric field data as 3D colorplot

vectorfield_color is using mlab.quiver3d to plot colored data-points in order to be able to fix the size of the points while varying the color-code

Parameters:	NF (-) – (X,Y,Z, Ex,Ey,Ez), the field components being complex. complex_part (-) – (default ‘real’) fieldComp (-) – if “I” is used, complex_part argument has no effect. scale (-) – abs_scale (-) – mode (-) – tit (-) – axis_labels (-) – show (-) –

other kwargs are passed to mlabs’s quiver3d

vectorfield_color_by_fieldindex¶

pyGDM2.visu3d.vectorfield_color_by_fieldindex(sim, field_index, **kwargs)¶

Wrapper to vectorfield_color(), using simulation object and fieldindex as input

Parameters:	sim (simulation) – instance of `core.simulation` field_index (int) – index of evaluated self-consistent field to use for calculation. Can be obtained for specific parameter-set using `tools.get_closest_field_index()`

:param All other keyword arguments are passed to vectorfield_color().:

scalarfield¶

pyGDM2.visu3d.scalarfield(NF, **kwargs)¶

Wrapper to vectorfield_color(), using scalar data tuples (x,y,z,S) as input

Parameters:	NF (list of 4-tuples) – list of tuples (x,y,z,S). Alternatively, the scalar-field can be passed as list of 2 lists containing the x/y positions and scalar values, respectively. ([xy-values, S], with xy-values: list of 2-tuples [x,y]; S: list of scalars). This format is returned e.g. by `tools.calculate_rasterscan()`.

:param All other keyword arguments are passed to vectorfield_color().:

animation¶

animate_vectorfield¶

pyGDM2.visu3d.animate_vectorfield(NF, Nframes=50, show=True, scale=1.5, abs_scale=False, draw_struct=False, draw_substrate=True, substrate_size=2.0, clim=[0.0, 1.0], figsize=(600, 400), save_anim=False, fig=None, view=(45, 45), ffmpeg_args='', mov_file='pygdm_animated_field.mp4', **kwargs)¶

tools¶

tools contains data-processing tools and helper functions

Save / Load / Print info¶

pyGDM2.tools.save_simulation(sim, fname, mode='h5', tables_filter_kwargs={'complevel': 5, 'complib': 'blosc'})¶

Save simulation object to file using pickle and hdf5 via tables

The electric fields are stored using hdf5 (via tables), the rest via pickle. If not tables module available, everything will be stored via pickle. This behavior can be forced with the mode parameter.

Parameters:	sim (`simulation`) – simulation description fname (str) – filename to save sim to mode (str, default: "hdf5") – which method to use to store data. Either “pickle” or “hd5f” (“h5”==”hd5f”) tables_filter_kwargs (dict, default: {'complib':'blosc', 'complevel':5}) – filter kwargs, passed to tables.Filters if using the “hdf5” mode

pyGDM2.tools.load_simulation(fname)¶

Load simulation from file using pickle

Try loading hdf5 first, if no hdf5 file or no tables module available, fallback to pickle.

Parameters:	fname (str) – filename to load sim from
Returns:
Return type:	instance of `core.simulation`

pyGDM2.tools.print_sim_info(sim, prnt=True, verbose=False)¶

print simulation info from simdict

Parameters:	sim (`simulation`) – simulation description prnt (bool, default: True) – If True, print to sdtout. If False, return string verbose (bool, default: False) – if True, print more details like exhaustive incident field parameter list

Notes

print_sim_info() is implemented as its __repr__ attribute in core.simulation. This means

>>> print sim

and

>>> print_sim_info(sim)

will result in the identical output

Field-index searching¶

pyGDM2.tools.get_field_indices(sim)¶

List all field-configurations including wavelength sorted by field-index

Parameters:	sim (`simulation`) – simulation description
Returns:	list of kwargs-dict containing field-config parameters. list in the order of the “field_index” convention.
Return type:	list of dict

pyGDM2.tools.get_closest_field_index(sim, search_kwargs)¶

Find closest calculated scattered field matching to search parameters

Parameters:	sim (`simulation`) – simulation description search_kwargs (dict) – searched kwargs for incident field
Returns:	index – index of field which matches closest search incident field parameters
Return type:	int

Structure geometry tools¶

pyGDM2.tools.get_geometry(struct, return_type='lists')¶

return coordinate lists X,Y,Z either from list of tuples or simulation object

Parameters:

Parameters:	struct (list of tuple or `core.simulation`) – list of coordinate tuples or instance of `core.simulation` projection (str, default: "XY") – 2D plane for projection. One of [‘XY’, ‘XZ’, ‘YZ’] return_type (str, default: 'lists') – either ‘lists’ or ‘tuples’ ’tuples’ : return list of (X,Y,Z)-tuples ’lists’ : return 3 lists with X, Y and Z values
Returns:	if return_type == ‘lists’ – np.array containing 3 np.arrays with the X, Y and Z coordinates if return_type == ‘tuples’ – list of 3-tuples : (x,y,z) coordinates of geometry

struct (list of tuple or core.simulation) – list of coordinate tuples or instance of core.simulation
projection (str, default: "XY") – 2D plane for projection. One of [‘XY’, ‘XZ’, ‘YZ’]
return_type (str, default: 'lists') –
either ‘lists’ or ‘tuples’
- ’tuples’ : return list of (X,Y,Z)-tuples
- ’lists’ : return 3 lists with X, Y and Z values

Returns:

if return_type == ‘lists’ – np.array containing 3 np.arrays with the X, Y and Z coordinates
if return_type == ‘tuples’ – list of 3-tuples : (x,y,z) coordinates of geometry

pyGDM2.tools.get_step_from_geometry(struct, max_meshpoints=1000)¶

Calculate step from coordinate list defining a nano-object

Will return closest distance occuring between two meshpoints. Uses scipy.spatial.distance.

Parameters:	struct (list of tuples or `core.simulation`) – max_meshpoints (int, default: 1000) – maximum number of meshpoints to consider for step-calculation if using scipy.spatial.distance. (for computational speed at large structures)
Returns:	step – stepsize between mesh-points
Return type:	float

pyGDM2.tools.get_geometry_2d_projection(struct, projection='XY')¶

return the geometry projection onto a 2D-plane

Parameters:	struct (list of tuples or `core.simulation`) – projection (str, default: "XY") – 2D plane for projection. One of [‘XY’, ‘XZ’, ‘YZ’]
Returns:	list of 3-tuples
Return type:	(x,y,z) coordinates of 2D-projection (third coordinate set to zero)

pyGDM2.tools.get_geometric_cross_section(struct, projection='XY', step=None)¶

return the geometrical cross-section (=’footprint’) of struct in nm

Parameters:	struct (list of tuples or `core.simulation`) – projection (str, default: "XY") – 2D plane for projection. One of [‘XY’, ‘XZ’, ‘YZ’] step (float, default: None) – optional pass step-size. If None, calculate it from geometry (may be slower)
Returns:	float
Return type:	geometric cross section of structure projection in nm^2

pyGDM2.tools.get_surface_meshpoints(struct, NN_bulk=6, max_bound=1.2, NN_surface=-1, max_bound_sf=5.0, return_sfvec_all_points=False)¶

get surface elements and normal surface vectors of structure

Calculate normal vectors using next-neighbor counting.

To use outmost surface layer only, parameters are:

2D: NN_bulk=4, max_bound=1.1
3D: NN_bulk=6, max_bound=1.1

Parameters:

Parameters:	struct (-) – list of tuples or instance of `core.simulation` NN_bulk (-) – Number of Next neighbors of a bulk lattice point max_bound (-) – Max. distance in step-units to search for next neighbors NN_surface (-) – different number of neighbours to consider for normal surfacevectors max_bound_sf (-) – different calculation range for normal surfacevectors. By default, use search radius of up to 5 steps. If a large number of neighbours should be considered for vector calculation, it might be necessary to increased this limit, which might however slow down the KD-tree queries. return_sfvec_all_points (-) – if True, return vector list for all meshpoints, with zero length if bulk
Returns:	- SF (list of surface meshpoint coordinates) - SF_vec (list of normal surface vectors)

struct (-) – list of tuples or instance of core.simulation
NN_bulk (-) – Number of Next neighbors of a bulk lattice point
max_bound (-) – Max. distance in step-units to search for next neighbors
NN_surface (-) – different number of neighbours to consider for normal surfacevectors
max_bound_sf (-) – different calculation range for normal surfacevectors. By default, use search radius of up to 5 steps. If a large number of neighbours should be considered for vector calculation, it might be necessary to increased this limit, which might however slow down the KD-tree queries.
return_sfvec_all_points (-) – if True, return vector list for all meshpoints, with zero length if bulk

Returns:

- SF (list of surface meshpoint coordinates)
- SF_vec (list of normal surface vectors)

pyGDM2.tools.get_closest_field_index(sim, search_kwargs)

Find closest calculated scattered field matching to search parameters

Parameters:	sim (`simulation`) – simulation description search_kwargs (dict) – searched kwargs for incident field
Returns:	index – index of field which matches closest search incident field parameters
Return type:	int

Spectra tools¶

pyGDM2.tools.get_possible_field_params_spectra(sim)¶

Return all possible field-parameter permutations for spectra

Parameters:	sim (`simulation`) – simulation description
Returns:	params – list of all possible parameter-permutation at each wavelength
Return type:	list of dict

pyGDM2.tools.calculate_spectrum(sim, field_kwargs, func, verbose=False, **kwargs)¶

calculate spectra using function func

Parameters:

Parameters:	sim (`simulation`) – simulation description field_kwargs (dict or int) – dict of field-configuration to use from simulation. If an int is given, it is used as field-index (use e.g. `get_closest_field_index()`) func (function) – evaluation function, which will be called as: func(sim, field_index, kwargs) kwargs – all other keyword args are passed to func
Returns:	np.array (wavelengths) np.array (results of func for each wavelength)

sim (simulation) – simulation description
field_kwargs (dict or int) – dict of field-configuration to use from simulation. If an int is given, it is used as field-index (use e.g. get_closest_field_index())
func (function) – evaluation function, which will be called as: func(sim, field_index, **kwargs)
**kwargs – all other keyword args are passed to func

Returns:

np.array (wavelengths)
np.array (results of func for each wavelength)

Raster-scan tools¶

pyGDM2.tools.get_possible_field_params_rasterscan(sim, key_x_pos='xSpot', key_y_pos='ySpot')¶

Return all possible field-parameter permutations for raster-scan simulations

Parameters:	sim (`simulation`) – simulation description key_x_pos (str, default: ‘xSpot’) – parameter-name defining the rasterscan beam/illumination x-position key_y_pos (str, default: ‘ySpot’) – parameter-name defining the rasterscan beam/illumination y-position
Returns:	params – list of dict for all possible parameter-permutation, defining rasterscan map
Return type:	list of dict

pyGDM2.tools.get_rasterscan_fields(sim, search_kwargs, key_x_pos='xSpot', key_y_pos='ySpot')¶

Return list of all field-parameter sets for a raster-scan

Parameters:	sim (`simulation`) – simulation description search_kwargs (dict) – searched kwargs for incident field key_x_pos (str, default: 'xSpot') – parameter-name defining the rasterscan beam/illumination x-position key_y_pos (str, default: 'ySpot') – parameter-name defining the rasterscan beam/illumination y-position
Returns:	NF_rasterscan – Each element corresponds to the simulated E-field inside the particle at a scan position: [field_param_dict, NF] - field_param_dict is the dict with the field-generator parameters - NF is the nearfield, as list of complex 3-tuples (Ex_i, Ey_i, Ez_i)
Return type:	list of lists

pyGDM2.tools.get_rasterscan_field_indices(sim, search_kwargs, key_x_pos='xSpot', key_y_pos='ySpot')¶

Return list of all field-parameter-indices corresponding to a raster-scan

Parameters:	sim (`simulation`) – simulation description search_kwargs (dict) – searched kwargs for incident field key_x_pos (str, default: 'xSpot') – parameter-name defining the rasterscan beam/illumination x-position key_y_pos (str, default: 'ySpot') – parameter-name defining the rasterscan beam/illumination y-position
Returns:	NF_rasterscan_indices – index of field which matches closest search incident field parameters
Return type:	list of int

pyGDM2.tools.calculate_rasterscan(sim, field_kwargs, func, key_x_pos='xSpot', key_y_pos='ySpot', verbose=False, **kwargs)¶

calculate rasterscan using function func

Parameters:

Parameters:	sim (`simulation`) – simulation description field_kwargs (dict or int) – dict of field-configuration to use from simulation. If an int is given, it is used as field-index (use e.g. `get_closest_field_index()`) func (function) – evaluation function, which will be called as: func(sim, field_index, kwargs) key_x_pos** (str, default: 'xSpot') – parameter-name defining the rasterscan beam/illumination x-position key_y_pos (str, default: 'ySpot') – parameter-name defining the rasterscan beam/illumination y-position **kwargs – all other keyword args are passed to func
Returns:	list of tuples (x,y) – positions of raster-scan evaluation list of float or tuple (S) – S is the scalar (or tuple) returned by func

sim (simulation) – simulation description
field_kwargs (dict or int) – dict of field-configuration to use from simulation. If an int is given, it is used as field-index (use e.g. get_closest_field_index())
func (function) – evaluation function, which will be called as: func(sim, field_index, **kwargs)
key_x_pos (str, default: 'xSpot') – parameter-name defining the rasterscan beam/illumination x-position
key_y_pos (str, default: 'ySpot') – parameter-name defining the rasterscan beam/illumination y-position
**kwargs – all other keyword args are passed to func

Returns:

list of tuples (x,y) – positions of raster-scan evaluation
list of float or tuple (S) – S is the scalar (or tuple) returned by func

pyGDM2.tools.get_possible_field_params_rasterscan(sim, key_x_pos='xSpot', key_y_pos='ySpot')

Return all possible field-parameter permutations for raster-scan simulations

Parameters:	sim (`simulation`) – simulation description key_x_pos (str, default: ‘xSpot’) – parameter-name defining the rasterscan beam/illumination x-position key_y_pos (str, default: ‘ySpot’) – parameter-name defining the rasterscan beam/illumination y-position
Returns:	params – list of dict for all possible parameter-permutation, defining rasterscan map
Return type:	list of dict

pyGDM2.tools.get_rasterscan_fields(sim, search_kwargs, key_x_pos='xSpot', key_y_pos='ySpot')

Return list of all field-parameter sets for a raster-scan

Parameters:	sim (`simulation`) – simulation description search_kwargs (dict) – searched kwargs for incident field key_x_pos (str, default: 'xSpot') – parameter-name defining the rasterscan beam/illumination x-position key_y_pos (str, default: 'ySpot') – parameter-name defining the rasterscan beam/illumination y-position
Returns:	NF_rasterscan – Each element corresponds to the simulated E-field inside the particle at a scan position: [field_param_dict, NF] - field_param_dict is the dict with the field-generator parameters - NF is the nearfield, as list of complex 3-tuples (Ex_i, Ey_i, Ez_i)
Return type:	list of lists

Coordinate-list transformations¶

pyGDM2.tools.unique_rows(a)¶

delete all duplicates with same x/y coordinates

Parameters:	a (list of tuples) – list of (x,y) or (x,y,z) coordinates with partially redunant (x,y) values.
Returns:	list of tuples in which all multiple tuples with equal first and second value (x,y coordinates) are removed. If 3-D data (Z-coordinate (3rd column) is replaced by 0.)

pyGDM2.tools.unique_rows_3D(a)¶

delete all duplicates with same x/y/z coordinates

Parameters:	a (list of tuples) – list of (x,y,z) coordinates with partially redunant (x,y) values.
Returns:	list of tuples in which all multiple tuples with equal (x,y,z) coordinates are removed.

pyGDM2.tools.adapt_map_to_structure_mesh(mapping, structure, projection=None, min_dist=1.5, nthreads=-1, verbose=False)¶

changes the positions of mapping coordinates close to meshpoints

All coordinates closer than a minimum (e.g. the stepsize) to a meshpoint of the nanostructure are replaced by the position of the closest meshpoint

Parameters:	mapping (list of 3-lists/-tuples) – list containing the mapping coordinates structure (list of coordinate 3-tuples or `core.simulation`) – instance of object containing the mapping information. projection (str, default: None) – consider some geometric projection. possible values: [‘XY’, ‘XZ’, ‘YZ’, None] if None: use closest meshpoint no matter which direction in space. If one of the strings, only consider meshpoints in the same plane as the mapping coordinates. min_dist (float, default: 1.5) – minimum distance to meshpoints in units of stepsize nthreads (int, default: all CPUs) – number of threads to work on in parallel verbose (bool, default: False) – print timing info
Returns:	coords – mapping with positions close to / inside the structure replaced by the nearest mesh-point coordinates
Return type:	list of 3-tuples

pyGDM2.tools.map_to_grid_2D(MAP, IVALUES, NX=-1, NY=-1)¶

Generate XY plottable map e.g. from 2D nearfield calculation

Parameters:

Parameters:	MAP (list of lists or list of tuples) – 2D MAP corresponding to data-array (e.g. from ‘generate_NF_map’). If list of lists : MAP[0] –> first coordinate; MAP[1] –> second coordinate. If list of tuples : list of (x,y) or (x,y,z) tuples. In the latter case, “z” will be ignored. IVALUES (list of floats) – Data-array: Intensity at each MAP position NY (NX,) – Nr of points to calculate for each dimension (do interpolation in nescessary)
Returns:	NF_MAP (2d-array) – nearfield map extent (tuple) – extent of map (x0,x1, y0,y1)

MAP (list of lists or list of tuples) –
2D MAP corresponding to data-array (e.g. from ‘generate_NF_map’).
- If list of lists :
  
  MAP[0] –> first coordinate; MAP[1] –> second coordinate.
- If list of tuples :
  
  list of (x,y) or (x,y,z) tuples. In the latter case, “z” will be ignored.
IVALUES (list of floats) – Data-array: Intensity at each MAP position
NY (NX,) – Nr of points to calculate for each dimension (do interpolation in nescessary)

Returns:

NF_MAP (2d-array) – nearfield map
extent (tuple) – extent of map (x0,x1, y0,y1)

pyGDM2.tools.get_field_as_list(NF, struct)¶

convert field NF and geometry struct to a list of coordinate/field tuples

Parameters:	NF (list of 3-tuples, complex) – field as returned e.g. from `core.scatter()` struct (list of tuples, float) – geometry of nano-object, as returned by strucutre-generated in module structures
Returns:	fieldlist – geometry and field in one list: [(x,y,z, Ex,Ey,Ez), (…), …]
Return type:	list of 6-tuples

pyGDM2.tools.get_field_as_list_by_fieldindex(sim, field_index)¶

Wrapper to get_field_as_list(), using simulation instance and fieldindex as input

see get_field_as_list() for more documentation

2D-mapping coordinate generators¶

pyGDM2.tools.generate_NF_map(X0, X1, NX, Y0, Y1, NY, Z0=0, projection='XY', dtype='f')¶

Generate MAP e.g. for nearfield calcuation on this map.

Parameters:	X0,X1 (-) – X-Limits for Map NX (-) – Nr of discretization pts in first projection direction Y0,Y1 (-) – Y-Limits for Map NY (-) – Nr of discretization pts in second projection direction Z0 (-) – Z-Height of map projection (-) – projection of map. must be one of [‘XY’,’XZ’,’YZ’], default: ‘XY’ dtype (-) – dtype of coordinates. passed to np.asfortranarray. e.g. “f” (single) or “d” (double). default: “f”
Returns:	list of 3 lists of X, Y, Z coordinates, to use e.g. in `linear.nearfield()`
Return type:	list of lists

pyGDM2.tools.generate_NF_map_XY(X0, X1, NX, Y0, Y1, NY, Z0=0, dtype='f')¶

Generate (XZ) MAP e.g. for nearfield calcuation on this map.

see generate_NF_map() for doc.

pyGDM2.tools.generate_NF_map_XZ(X0, X1, NX, Z0, Z1, NZ, Y0=0, dtype='f')¶

Generate (XZ) MAP e.g. for nearfield calcuation on this map.

see generate_NF_map() for doc.

pyGDM2.tools.generate_NF_map_YZ(Y0, Y1, NY, Z0, Z1, NZ, X0=0, dtype='f')¶

Generate (YZ) MAP e.g. for nearfield calcuation on this map.

see generate_NF_map() for doc.

Other¶

pyGDM2.tools.evaluate_incident_field(field_generator, wavelength, kwargs, r_probe, n1=1.0, n2=1.0, n3=None, spacing=5000.0)¶

Evaluate an incident field generator

Calculate the field defined by a field_generator function for a given set of coordinates.

Parameters:	field_generator (callable) – field generator function. Mandatory arguments are struct (instance of `structures.struct`) wavelength (list of wavelengths) wavelength (float) – wavelength in nm kwargs (dict) – optional kwargs for the field_generator functions (see also `fields.efield`). r_probe (tuple (x,y,z) or list of 3-lists/-tuples) – (list of) coordinate(s) to evaluate nearfield on. Format: tuple (x,y,z) or list of 3 lists: [Xmap, Ymap, Zmap] (the latter can be generated e.g. using `tools.generate_NF_map()`) n1,n2,n3 (complex, default: 1.0, 1.0, None) – indices of 3 layered media. if n3==None: n3=n2. spacing (float, default: 5000) – distance between substrate and cladding (=thickness of layer “n2”)
Returns:	field_list – field at coordinates as list of tuples (x,y,z, Ex,Ey,Ez)
Return type:	list of tuples