ValidBaselineSubset

class pyvisgen.simulation.ValidBaselineSubset(u_start: tensor, u_stop: tensor, u_valid: tensor, v_start: tensor, v_stop: tensor, v_valid: tensor, w_start: tensor, w_stop: tensor, w_valid: tensor, baseline_nums: tensor, date: tensor, q1_start: tensor, q1_stop: tensor, q1_valid: tensor, q2_start: tensor, q2_stop: tensor, q2_valid: tensor)

Bases: object

Valid baselines subset dataclass. Attributes ending on valid are all quantities where at least one baseline pair has contributed to the measurement of the source. Attributes ending on start are starting points for integration windows that end with attributes ending on stop.

Attributes:

u_starttensor()

Start value for u coverage integration.

u_stoptensor()

Stop value for u coverage integration.

u_validtensor()

Valid u values.

v_starttensor()

Start value for v coverage integration.

v_stoptensor()

Start value for v coverage integration.

v_validtensor()

Valid v values.

w_starttensor()

Start value for w coverage integration.

w_stoptensor()

Start value for w coverage integration.

w_validtensor()

Valid w values.

baseline_numstensor()

Numbers of baselines per time step.

datetensor()

Time steps of the measurement during which at least one baseline pair contributed to the measurement.

q1_starttensor()

q1_stoptensor()

q1_validtensor()

Valid parallactic angle values (first half of the pair).

q2_starttensor()

q2_stoptensor()

q2_validtensor()

Valid parallactic angle values (second half of the pair).

Attributes Summary

baseline_nums
date
q1_start
q1_stop
q1_valid
q2_start
q2_stop
q2_valid
u_start
u_stop
u_valid
v_start
v_stop
v_valid
w_start
w_stop
w_valid

Methods Summary

get_timerange(t_start, t_stop)	Returns all attributes that fall into the time range [t_start, t_stop].
get_unique_grid(fov, ref_frequency, ...)	Returns the unique grid for a given FOV, frequency, and image size.

Attributes Documentation

baseline_nums: tensor = <dataclasses._MISSING_TYPE object>

date: tensor = <dataclasses._MISSING_TYPE object>

q1_start: tensor = <dataclasses._MISSING_TYPE object>

q1_stop: tensor = <dataclasses._MISSING_TYPE object>

q1_valid: tensor = <dataclasses._MISSING_TYPE object>

q2_start: tensor = <dataclasses._MISSING_TYPE object>

q2_stop: tensor = <dataclasses._MISSING_TYPE object>

q2_valid: tensor = <dataclasses._MISSING_TYPE object>

u_start: tensor = <dataclasses._MISSING_TYPE object>

u_stop: tensor = <dataclasses._MISSING_TYPE object>

u_valid: tensor = <dataclasses._MISSING_TYPE object>

v_start: tensor = <dataclasses._MISSING_TYPE object>

v_stop: tensor = <dataclasses._MISSING_TYPE object>

v_valid: tensor = <dataclasses._MISSING_TYPE object>

w_start: tensor = <dataclasses._MISSING_TYPE object>

w_stop: tensor = <dataclasses._MISSING_TYPE object>

w_valid: tensor = <dataclasses._MISSING_TYPE object>

Methods Documentation

get_timerange(t_start, t_stop)

Returns all attributes that fall into the time range [t_start, t_stop].

Parameters:

t_startdatetime

Start date.

t_stopdatetime

End date.

Returns:

ValidBaselineSubset

ValidBaselineSubset dataclass object containing all attributes that fall in the time range between t_start and t_stop.

get_unique_grid(fov: float, ref_frequency: float, img_size: int, device: str)

Returns the unique grid for a given FOV, frequency, and image size.

Parameters:

fovfloat

Size of the FOV.

ref_frequencyfloat

Reference frequency.

img_sizeint

Size of the image.

devicestr

Name of the device to run the operation on, e.g. 'cuda' or 'cpu'.

Returns:

torch.tensor

Tensor containing the unique grid for a given FOV, frequency, and image size.