mrpro.operators.models.EPG.Parameters

class mrpro.operators.models.EPG.Parameters[source]

Bases: Dataclass

Tissue parameters for EPG simulation.

__init__(m0: Tensor, t1: Tensor, t2: Tensor, relative_b1: Tensor | None = None) → None
relative_b1: Tensor | None = None

Relative B1 scaling factor (complex)

m0: Tensor

Steay state magnetization (complex)

t1: Tensor

T1 relaxation time [s]

t2: Tensor

T2 relaxation time [s]

property device: device[source]

Device of the parameters.

property dtype: dtype[source]

Promoted data type of the parameters.

property ndim: int[source]

Number of dimensions of the parameters.

property shape: Size[source]

Broadcasted shape of the parameters.

property is_cpu: bool[source]

Return True if all tensors are on the CPU.

Checks all tensor attributes of the dataclass for their device, (recursively if an attribute is a Dataclass)

Returns False if not all tensors are on cpu or if the device is inconsistent, returns True if the data class has no tensors as attributes.

property is_cuda: bool[source]

Return True if all tensors are on a single CUDA device.

Checks all tensor attributes of the dataclass for their device, (recursively if an attribute is a Dataclass)

Returns False if not all tensors are on the same CUDA devices, or if the device is inconsistent, returns True if the data class has no tensors as attributes.

apply(function: Callable[[Any], Any] | None = None, *, recurse: bool = True) → Self[source]

Apply a function to all children. Returns a new object.

Parameters:

  • function (Callable[[Any], Any] | None, default: None) – The function to apply to all fields. None is interpreted as a no-op.

  • recurse (bool, default: True) – If True, the function will be applied to all children that are Dataclass instances.

apply_(function: Callable[[Any], Any] | None = None, *, memo: dict[int, Any] | None = None, recurse: bool = True) → Self[source]

Apply a function to all children in-place.

Parameters:

  • function (Callable[[Any], Any] | None, default: None) – The function to apply to all fields. None is interpreted as a no-op.

  • memo (dict[int, Any] | None, default: None) – A dictionary to keep track of objects that the function has already been applied to, to avoid multiple applications. This is useful if the object has a circular reference.

  • recurse (bool, default: True) – If True, the function will be applied to all children that are Dataclass instances.

clone() → Self[source]

Return a deep copy of the object.

cpu(*, memory_format: memory_format = torch.preserve_format, copy: bool = False) → Self[source]

Put in CPU memory.

Parameters:

  • memory_format (memory_format, default: torch.preserve_format) – The desired memory format of returned tensor.

  • copy (bool, default: False) – If True, the returned tensor will always be a copy, even if the input was already on the correct device. This will also create new tensors for views.

cuda(device: device | str | int | None = None, *, non_blocking: bool = False, memory_format: memory_format = torch.preserve_format, copy: bool = False) → Self[source]

Put object in CUDA memory.

Parameters:

  • device (device | str | int | None, default: None) – The destination GPU device. Defaults to the current CUDA device.

  • non_blocking (bool, default: False) – If True and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect.

  • memory_format (memory_format, default: torch.preserve_format) – The desired memory format of returned tensor.

  • copy (bool, default: False) – If True, the returned tensor will always be a copy, even if the input was already on the correct device. This will also create new tensors for views.

double(*, memory_format: memory_format = torch.preserve_format, copy: bool = False) → Self[source]

Convert all float tensors to double precision.

converts float to float64 and complex to complex128

Parameters:

  • memory_format (memory_format, default: torch.preserve_format) – The desired memory format of returned tensor.

  • copy (bool, default: False) – If True, the returned tensor will always be a copy, even if the input was already on the correct device. This will also create new tensors for views.

half(*, memory_format: memory_format = torch.preserve_format, copy: bool = False) → Self[source]

Convert all float tensors to half precision.

converts float to float16 and complex to complex32

Parameters:

  • memory_format (memory_format, default: torch.preserve_format) – The desired memory format of returned tensor.

  • copy (bool, default: False) – If True, the returned tensor will always be a copy, even if the input was already on the correct device. This will also create new tensors for views.

items() → Iterator[tuple[str, Any]][source]

Get an iterator over names and values of fields.

single(*, memory_format: memory_format = torch.preserve_format, copy: bool = False) → Self[source]

Convert all float tensors to single precision.

converts float to float32 and complex to complex64

Parameters:

  • memory_format (memory_format, default: torch.preserve_format) – The desired memory format of returned tensor.

  • copy (bool, default: False) – If True, the returned tensor will always be a copy, even if the input was already on the correct device. This will also create new tensors for views.

to(device: str | device | int | None = None, dtype: dtype | None = None, non_blocking: bool = False, *, copy: bool = False, memory_format: memory_format | None = None) → Self[source]
to(dtype: dtype, non_blocking: bool = False, *, copy: bool = False, memory_format: memory_format | None = None) → Self
to(tensor: Tensor, non_blocking: bool = False, *, copy: bool = False, memory_format: memory_format | None = None) → Self

Perform dtype and/or device conversion of data.

A torch.dtype and torch.device are inferred from the arguments args and kwargs. Please have a look at the documentation of torch.Tensor.to for more details.

A new instance of the dataclass will be returned.

The conversion will be applied to all Tensor- or Module fields of the dataclass, and to all fields that implement the Dataclass.

The dtype-type, i.e. float or complex will always be preserved, but the precision of floating point dtypes might be changed.

Example: If called with dtype=torch.float32 OR dtype=torch.complex64:

  • A complex128 tensor will be converted to complex64

  • A float64 tensor will be converted to float32

  • A bool tensor will remain bool

  • An int64 tensor will remain int64

If other conversions are desired, please use the to method of the fields directly.

If the copy argument is set to True (default), a deep copy will be returned even if no conversion is necessary. If two fields are views of the same data before, in the result they will be independent copies if copy is set to True or a conversion is necessary. If set to False, some Tensors might be shared between the original and the new object.

__eq__(other)

Return self==value.

__new__(**kwargs)