prefs

Preferences and configuration shared throughout the program.

Saves and loads to a prefs file (default is ~/.perceptivo/prefs.json )

Each runtime has its own set of preferences. When first run, if there is not prefs file detected it populates with defaults (though defaults can be populated at any time by instantiating the object with no arguments and using save, eg.:

prefs = Patient_Prefs()
prefs.save()

class perceptivo.prefs.Runtimes(value)

Bases: enum.Enum

An enumeration.

patient = 'patient'

clinician = 'clinician'

stimuli = 'stimuli'

perceptivo.prefs.json_dumps_pretty(v, *, default)

class perceptivo.prefs.Prefs(*, loglevel: Literal['DEBUG', 'INFO', 'WARNING', 'ERROR'] = 'DEBUG')

Bases: pydantic.main.BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

loglevel: Literal['DEBUG', 'INFO', 'WARNING', 'ERROR']

class Config

Bases: object

json_dumps(*, default)

save(file: pathlib.Path = PosixPath('/home/docs/.perceptivo/prefs.json'))

classmethod load(file: pathlib.Path = PosixPath('/home/docs/.perceptivo/prefs.json')) → perceptivo.prefs.Prefs

classmethod get_runtime_prefs(runtime: perceptivo.prefs.Runtimes) → perceptivo.prefs.Patient_Prefs

class perceptivo.prefs.Patient_Prefs(*, loglevel: Literal['DEBUG', 'INFO', 'WARNING', 'ERROR'] = 'DEBUG', runtime: perceptivo.prefs.Runtimes = 'patient', Audio_Config: perceptivo.types.sound.Audio_Config = Audio_Config(fs=44100), Audiogram_Model: perceptivo.types.psychophys.Psychoacoustic_Model = Psychoacoustic_Model(model_type='Gaussian_Process', args=[], kwargs={'kernel': Kernel(length_scale=(100.0, 200.0), length_scale_bounds=(1, 100000.0))}), Picamera_Params: perceptivo.types.video.Picamera_Params = Picamera_Params(sensor_mode=0, resolution=(1280, 720), fps=30, format='grayscale', output_file=None), picamera_process: bool = True, picam_queue_size: int = 1024, pupil_extractor: str = 'simple', pupil_extractor_params: perceptivo.video.pupil.EllipseExtractor_Params = EllipseExtractor_Params(footprint_size=5, search_scale=1.5), collection_params: perceptivo.types.patient.Collection_Params = Collection_Params(collection_wait=5), networking: perceptivo.types.networking.Patient_Networking = Patient_Networking(ip='', clinician_ip='', eyecam=Socket(id='patient:eyecam', socket_type='PUSH', protocol='tcp', mode='connect', port=5500, ip='', to=None), control=Socket(id='patient:control', socket_type='DEALER', protocol='tcp', mode='connect', port=5600, ip='', to='clinician:control')))

Bases: perceptivo.prefs.Prefs

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

runtime: perceptivo.prefs.Runtimes

Audio_Config: perceptivo.types.sound.Audio_Config

Audiogram_Model: perceptivo.types.psychophys.Psychoacoustic_Model

Picamera_Params: perceptivo.types.video.Picamera_Params

picamera_process: bool

Run the picamera in a separate Process (using cameras.Picamera_Process . Only True supported for now!

picam_queue_size: int

pupil_extractor: str

pupil_extractor_params: perceptivo.video.pupil.EllipseExtractor_Params

collection_params: perceptivo.types.patient.Collection_Params

networking: perceptivo.types.networking.Patient_Networking

class Config

Bases: object

use_enum_values = True

class perceptivo.prefs.Clinician_Prefs(*, loglevel: Literal['DEBUG', 'INFO', 'WARNING', 'ERROR'] = 'DEBUG', networking: perceptivo.types.networking.Clinician_Networking = Clinician_Networking(ip='', patient_ip='', eyecam=Socket(id='clinician:eyecam', socket_type='PULL', protocol='tcp', mode='bind', port=5500, ip='*', to=None), control=Socket(id='clinician:control', socket_type='ROUTER', protocol='tcp', mode='bind', port=5600, ip='*', to=None)), gui: perceptivo.types.gui.GUI_Params = GUI_Params(control_panel=Control_Panel_Params(amplitude_range=GUI_Param(key='amplitude_range', name='Amplitude Range (dBSPL)', widget_type='range', default=GUI_Range(min=0.0, max=80.0, n=8), args=[], kwargs={'limits': (0, 100)}), frequency_range=GUI_Param(key='frequency_range', name='Frequency Range (Hz)', widget_type='range', default=GUI_Range(min=0.0, max=8000.0, n=17), args=[], kwargs={'limits': (0, 20000)}), iti=GUI_Param(key='iti', name='Inter-Trial Interval (s)', widget_type='float', default=5.0, args=[], kwargs={}), iti_jitter=GUI_Param(key='iti_jitter', name='Inter-Trial Jitter (proportion of ITI)', widget_type='float', default=0.1, args=[], kwargs={}))), update_period: float = 0.05)

Bases: perceptivo.prefs.Prefs

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

networking: perceptivo.types.networking.Clinician_Networking

gui: perceptivo.types.gui.GUI_Params

update_period: float

perceptivo.prefs.get(field: str, file: pathlib.Path = PosixPath('/home/docs/.perceptivo/prefs.json'))

perceptivo.prefs.set_global(prefs: perceptivo.prefs.Prefs)

perceptivo.prefs.get_global() → perceptivo.prefs.Prefs