de_twin.column.MeasurementResult#

class de_twin.column.MeasurementResult(kind, aberrations, sigma_nm, tab_type='', angle_mrad=0.0, max_fit='', images=1, images_lost=0, t_start=0.0, t_done=0.0, side='probe', names=())[source]#

Bases: object

A tableau / C1A1 measurement, as the corrector reports it (plus confidence).

Parameters:

kind (str)
aberrations (Aberrations)
sigma_nm (dict[str, float])
tab_type (str)
angle_mrad (float)
max_fit (str)
images (int)
images_lost (int)
t_start (float)
t_done (float)
side (str)
names (tuple)

__init__(kind, aberrations, sigma_nm, tab_type='', angle_mrad=0.0, max_fit='', images=1, images_lost=0, t_start=0.0, t_done=0.0, side='probe', names=())#

Parameters:

kind (str)
aberrations (Aberrations)
sigma_nm (dict[str, float])
tab_type (str)
angle_mrad (float)
max_fit (str)
images (int)
images_lost (int)
t_start (float)
t_done (float)
side (str)
names (tuple)

Return type:

None

Methods

`__init__`(kind, aberrations, sigma_nm[, ...])
`ceos`()	[(name, (x_m, y_m))] in CEOS order (the AberrationSet on the wire).
`pi4_angle_mrad`([ht_kv])
`to_string`()	`AberrationSet::toString`: `C1=x,y;A1=x,y;...` metres, `%.9g`.

Attributes

`angle_mrad`
`images`
`images_lost`
`max_fit`
`names`
`side`
`t_done`
`t_start`
`tab_type`
`kind`
`aberrations`
`sigma_nm`

kind: str#

aberrations: Aberrations#

sigma_nm: dict[str, float]#

tab_type: str = ''#

angle_mrad: float = 0.0#

max_fit: str = ''#

images: int = 1#

images_lost: int = 0#

t_start: float = 0.0#

t_done: float = 0.0#

side: str = 'probe'#

names: tuple = ()#

ceos()[source]#

[(name, (x_m, y_m))] in CEOS order (the AberrationSet on the wire).

Return type:: list[tuple[str, tuple[float, float]]]

to_string()[source]#

AberrationSet::toString: C1=x,y;A1=x,y;... metres, %.9g.

Return type:: str

pi4_angle_mrad(ht_kv=200.0)[source]#

Parameters:: ht_kv (float)
Return type:: float