stimpyp.pyvstim.StimlogPyVStim

final class stimpyp.pyvstim.StimlogPyVStim

Bases: AbstractStimlog

class for handle the log file (stim event specific) for pyvstim version (vb lab legacy)

Dimension parameters:

N = number of visual stimulation (on-off pairs) = (T * S)

T = number of trials

S = number of Stim Type

P = number of acquisition sample pulse

__init__(riglog)

Parameters:

• riglog (PyVlog) – Baselog

• file_path – filepath of stimlog. could be None if shared log (pyvstim case)

• reset_mapping – Customized mapping for reset() - key: corresponding to log_header - value: list of field, should be the same name as class annotations

Methods

__init__(riglog)
exp_end_time()	experimental end time (in sec, synced to riglog time with diode offset)
get_log_dict_dataframe()	Log dict dataframe.
get_photo_indicator_dataframe()	Photo Indicator dataframe.
get_state_machine_dataframe()	State Machine dataframe
get_stim_pattern()	get pattern foreach stimulation
get_visual_stim_dataframe(**kwargs)	Visual presentation dataframe
plot_stim_animation()
session_trials()	get the session:SessionInfo dictionary (experimental and user-specific)
stim_square_pulse_event([sampling_rate])	Get the stimulation on-off square pulse 0,1 consecutive event

Attributes

avg_refresh_rate	in Hz
config	config for non-header information
exp_start_time	experimental start time (in sec, synced to riglog time with diode offset)
log_header	code:log header dictionary
log_info	code:log name dictionary
n_cycles	Number of cycle for each trial
profile_dataframe	Dataframe with columns:
stim_end_time	the last stimulation end time (in sec, synced to riglog time with diode offset)
stim_loc
stim_start_time	the first stimulation start time (in sec, synced to riglog time with diode offset)
stimulus_segment	stimulation time segment (on-off) in sec (N, 2)
time_offset	directly used interpolation using diode signal already
frame	TBD.
blank	If it is background only.
disp_x	TBD.
disp_y	TBD.
photo_state	photo diode on-off.
v_duino_time	extrapolate duinotime from screen indicator.
time	acquisition time in sec.
stim_index	stimulation index.
trial_index	trial index.
contrast	stimulus contrast.
frame_index	stimulation frame index, recount every N.
ori	direction in deg.
sf	spatial frequency in cyc/deg.
tf	temporal frequency in hz.
phase	stimulus phase for each N.
pos_x	object center position X.
pos_y	object center position Y.
size_x	object size width.
size_y	object size height.
flick	TODO.
interpolate	whether do the interpolate, Array[bool, P]
mask	TODO.
pattern	object pattern.

frame: ndarray

TBD. value domain (-2, -1, 0, 1) ? Array[int, P]

blank: ndarray

If it is background only. 0: stim display, 1: no stim. Array[int, P]

disp_x: ndarray

TBD. display pos x ? Array[int, P]

disp_y: ndarray

TBD. display pos y ? Array[int, P]

photo_state: ndarray

photo diode on-off. Array[int, P]. value domain in (0,1)

v_duino_time: ndarray

extrapolate duinotime from screen indicator. sync arduino time in sec. Array[float, P]

__init__(riglog)

Parameters:

• riglog (PyVlog) – Baselog

• file_path – filepath of stimlog. could be None if shared log (pyvstim case)

• reset_mapping – Customized mapping for reset() - key: corresponding to log_header - value: list of field, should be the same name as class annotations

config: Final[dict[str, Any]] = {}

config for non-header information

log_info: Final[dict[int, str]] = {}

code:log name dictionary

log_header: Final[dict[int, list[str]]] = {}

code:log header dictionary

get_visual_stim_dataframe(**kwargs)

Visual presentation dataframe

Return type:

DataFrame

get_state_machine_dataframe()

State Machine dataframe

property exp_start_time: float

experimental start time (in sec, synced to riglog time with diode offset)

property stimulus_segment: ndarray

stimulation time segment (on-off) in sec (N, 2)

session_trials()

get the session:SessionInfo dictionary (experimental and user-specific)

Return type:

dict[str, SessionInfo]

property time_offset: float

directly used interpolation using diode signal already

get_stim_pattern()

get pattern foreach stimulation

Return type:

GratingPattern

exp_end_time()

experimental end time (in sec, synced to riglog time with diode offset)

Return type:

float

property stim_loc: ndarray

property avg_refresh_rate: float

in Hz

plot_stim_animation()

property profile_dataframe: DataFrame

Dataframe with columns:

• stim type index: i_stims

• trial index: i_trials

property n_cycles: list[int]

Number of cycle for each trial

get_log_dict_dataframe()

Log dict dataframe. Github version only

Return type:

DataFrame

get_photo_indicator_dataframe()

Photo Indicator dataframe. Github version only

Return type:

DataFrame

property stim_end_time: float

the last stimulation end time (in sec, synced to riglog time with diode offset)

stim_square_pulse_event(sampling_rate=30.0)

Get the stimulation on-off square pulse 0,1 consecutive event

Parameters:

sampling_rate (float) – sampling rate for the time domain interpolation

Returns:

Stimulus rig event

Return type:

RigEvent

property stim_start_time: float

the first stimulation start time (in sec, synced to riglog time with diode offset)

time: ndarray

acquisition time in sec. Array[float, P]

stim_index: ndarray

stimulation index. Array[int, P]

trial_index: ndarray

trial index. Array[int, P]

contrast: ndarray

stimulus contrast. Array[int, P]. value domain in (0,1)

frame_index: ndarray

stimulation frame index, recount every N. Array[int, P]

ori: ndarray

direction in deg. Array[float, P]

sf: ndarray

spatial frequency in cyc/deg. Array[float, P]

tf: ndarray

temporal frequency in hz. Array[float, P]

phase: ndarray

stimulus phase for each N. Array[float, P]

pos_x: ndarray

object center position X. Array[float, P]

pos_y: ndarray

object center position Y. Array[float, P]

size_x: ndarray

object size width. Array[int, P]

size_y: ndarray

object size height. Array[int, P]

flick: ndarray

TODO. Array[int, P]

interpolate: ndarray

whether do the interpolate, Array[bool, P]

mask: ndarray

TODO. Array[bool|None, P]

pattern: ndarray

object pattern. Array[str, P]