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:

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:

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', clim=[0.0, 1.0], 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:

: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:

: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:
  • 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:
  • 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:
  • 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:
  • 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 index on results of get_possible_field_params_rasterscan()
  • 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:
  • 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.grid_to_list(arr2d, extent=None, add_z=False)

create list of X/Y/S tuples from 2D map data

Parameters:
  • arr2d (2D np.array) – 2D data array of shape (Nx, Ny)
  • extent (4-tuple, default: None) – X / Y limits of array as tuple (x0, x1, y0, y1). If None, uses integer steps between 0 and Nx, Ny.
  • add_z (bool, default: False) – if true, returns (x,y,z,S) tuples, with z=0
Returns:

scalarfield – list of (x,y,S) values, where S are the values of the original 2D map arr2d
Return type:

np.array
pyGDM2.tools.get_field_as_list(NF, struct)

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

Note, that usually it is easier to use get_field_as_list_by_fieldindex() instead.

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 structure 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)

return internal fields in structure as list of (x,y,z,Ex,Ey,Ez) tuples

Parameters:
  • sim (simulation) – simulation description
  • field_index (int) – field-index corresponding to the incident field configuration
Returns:

fieldlist – geometry and field in one list: [(x,y,z, Ex,Ey,Ez), (…), …]
Return type:

list of 6-tuples
pyGDM2.tools.get_intensity_from_fieldlist(NF_list)

calculate intensity values from complex fields in field-vector-list

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 structure generated in module structures
Returns:

fieldlist – list of tuples containing coordinates and field-intensity: [(x,y,z, I), (…), …]
Return type:

list of 4-tuples

2D-mapping coordinate generators

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

Generate coordinate list with equidistant positions on a rectangular 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 coordinate list with equidistant positions on a rectangular map in XY plane

see generate_NF_map() for doc.

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

Generate coordinate list with equidistant positions on a rectangular map in XZ plane

see generate_NF_map() for doc.

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

Generate coordinate list with equidistant positions on a rectangular map in YZ plane

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
  • 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
pyGDM2.tools.get_intensity_from_fieldlist(NF_list)

calculate intensity values from complex fields in field-vector-list

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 structure generated in module structures
Returns:

fieldlist – list of tuples containing coordinates and field-intensity: [(x,y,z, I), (…), …]
Return type:

list of 4-tuples