ObservedShell

from mcot.dippi.scan import ObservedShell
class mcot.dippi.scan.ObservedShell(gradients: numpy.ndarray, scanner: mcot.dippi.scan.Scanner = <factory>, bval: float = 3.0, dual_echo: bool = True, t_phase: float = 30, b0_dir: numpy.ndarray = array([0., 0., 1.]), b0_group: int = 0, TE1: float = 80, TE2: float = 120)[source]

Represents an shell of actual observed data, where the first and second echo time are known.

To simulate realistic echo times use SimulatedShell instead.

__init__(gradients: numpy.ndarray, scanner: mcot.dippi.scan.Scanner = <factory>, bval: float = 3.0, dual_echo: bool = True, t_phase: float = 30, b0_dir: numpy.ndarray = array([0., 0., 1.]), b0_group: int = 0, TE1: float = 80, TE2: float = 120) → None

Inheritance diagram

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Methods

from_dict(kvs, *[, infer_missing])

from_json(s, *[, parse_float, parse_int, ...])

schema(*[, infer_missing, only, exclude, ...])

spherical_harmonics(lmax[, odd])

to_dict([encode_json])

to_json(*[, skipkeys, ensure_ascii, ...])

Attributes

TE1

Time of first spin echo in ms

TE2

Time of second spin echo in ms

b0_dir

direction of the B0 field

b0_group

Give each shell that should be considered a different orientation a different b0_group

bval

b-value in ms/micrometer^2

dataclass_json_config

dual_echo

Whether to add a second refocus pulse

ngradients

readout1

Time of first readout in ms

readout2

Time of second readout in ms

t_phase

time of phase accumulation

gradients

Orientation of diffusion-weighted gradients in (N, 3) array

scanner

Properties of the scanner on which the data was acquired

from_dict

classmethod ObservedShell.from_dict(kvs: Optional[Union[dict, list, str, int, float, bool]], *, infer_missing=False) → dataclasses_json.api.A

from_json

classmethod ObservedShell.from_json(s: Union[str, bytes, bytearray], *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw) → dataclasses_json.api.A

schema

classmethod ObservedShell.schema(*, infer_missing: bool = False, only=None, exclude=(), many: bool = False, context=None, load_only=(), dump_only=(), partial: bool = False, unknown=None) → dataclasses_json.mm.SchemaF[dataclasses_json.mm.A]

spherical_harmonics

ObservedShell.spherical_harmonics(lmax, odd=True)[source]

to_dict

ObservedShell.to_dict(encode_json=False) → Dict[str, Optional[Union[dict, list, str, int, float, bool]]]

to_json

ObservedShell.to_json(*, skipkeys: bool = False, ensure_ascii: bool = True, check_circular: bool = True, allow_nan: bool = True, indent: Optional[Union[int, str]] = None, separators: Optional[Tuple[str, str]] = None, default: Optional[Callable] = None, sort_keys: bool = False, **kw) → str