SurfaceSequence

class dnois.optics.rt.SurfaceSequence(surfaces: Sequence[Surface] = None, foremost_material: Material | str = 'vacuum', stop_idx: int = None)

A sequential container of surfaces. This class is derived from torch.nn.ModuleList and implements collections.abc.MutableSequence interface. So its instance can be regarded as both a PyTorch module and a list of Surface.

Parameters:

  • surfaces (Sequence[Surface]) – A sequence of Surface objects. Default: [].

  • foremost_material (Material) – The material before the first surface.

extra_repr() → str

Return the extra representation of the module.

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

forward(ray: BatchedRay, forward: bool = True) → BatchedRay

Traces rays incident on the first surface and returns rays passing the last surface, or reversely if forward is False.

Parameters:

  • ray (BatchedRay) – Input rays.

  • forward (bool) – Whether rays are forward or not.

Returns:

Output rays.

Return type:

BatchedRay

freeze(name: str | Sequence[str] = None)

Equivalent to self.set_optimizable(name, False). See set_optimizable().

classmethod from_dict(d: dict)

Constructs an instance of cls from a dict.

Parameters:

d (dict) – A dict typically returned by to_dict().

Returns:

An instance of cls.

classmethod load_json(file, **kwargs) → Self

Constructs an instance of cls through loading JSON from a file, converting it to a dict and then calling from_dict().

Parameters:

  • file (str or pathlib.Path or file-like object) – The JSON file to load. Either its path (str or pathlib.Path) or a file-like object.

  • kwargs – Keyword arguments passed to json.load().

Returns:

An instance of cls.

paraxialize(wl: Real | Tensor) → ParaxialSystem

Apply paraxialize() to this surface sequence.

save_json(file, **kwargs)

Save self into a JSON file file.

Parameters:

  • file – The JSON file to save. Either its path (str or pathlib.Path) or a file-like object.

  • kwargs – Keyword arguments passed to json.dump().

set_optimizable(name: str | Sequence[str] = None, optimizable: bool = True)

Specify whether a parameter is optimizable.

Parameters:

  • name (str) – Name of the parameter. If None, all parameters will be set. It follows the same convention as torch.nn.Module.get_parameter().

  • optimizable (bool) – Whether the specified parameter is optimizable. Default: True.

to_dict(keep_tensor=True) → dict[str, Any]

Converts self into a dict which recursively contains only primitive Python objects.

Return type:

dict

to_json(**kwargs) → str

Converts self into a JSON string.

Parameters:

kwargs – Keyword arguments passed to json.dumps().

Return type:

str

unfreeze(name: str | Sequence[str] = None)

Equivalent to self.set_optimizable(name, True). See set_optimizable().

property first: Surface

Returns the first surface. This property can be set or deleted.

Type:

Surface.

property is_empty: bool

Whether this list is empty.

Type:

bool

property last: Surface

Returns the last surface. This property can be set or deleted.

Type:

Surface.

mt_head: mt.Material

Material before the first surface.

property mt_tail: Material

Returns the material after the last surface.

Type:

Material

property stop: CircularStop | None

The aperture stop object. Returns None if no stop is found. Note that it need not return an instance of CircularStop.

Type:

CircularStop or None

property stop_idx: int | None

Index of the aperture stop. Returns None if no stop is found.

Type:

int or None