psychophys

class perceptivo.types.psychophys.Sample(sound: perceptivo.types.sound.Sound, dilation: typing.Optional[perceptivo.types.pupil.Dilation] = None, timestamp: datetime.datetime = <factory>, response: dataclasses.InitVar[bool] = <property object>)

Bases: object

A single sample of a psychophysical response to a sound

Variables

• ~Sample.dilation (types.pupil.Dilation) – Pupil object storing dilation for a given sample

• ~Sample.sound (types.sound.Sound) – Sound presented to elicit Pupil response

• ~Sample.timestamp (datetime.datetime) – Timestamp at which the response was elicited

Properties:

response (bool): Sub/Subtrathreshold response from Pupil.response

sound: perceptivo.types.sound.Sound

dilation: Optional[perceptivo.types.pupil.Dilation] = None

timestamp: datetime.datetime

property response: bool

class perceptivo.types.psychophys.Samples(samples: Optional[List[perceptivo.types.psychophys.Sample]] = None, dilations: Optional[List[perceptivo.types.pupil.Dilation]] = None, frequencies: Optional[List[float]] = None, amplitudes: Optional[List[float]] = None, responses: Optional[List[bool]] = None)

Bases: object

Multiple Samples!

Convenience class to init samples from numpy arrays and convert to pandas dataframe

samples: List[perceptivo.types.psychophys.Sample]

responses: List[bool]

frequencies: List[float]

amplitudes: List[float]

append(sample: perceptivo.types.psychophys.Sample)

Add a sample to the collection

Parameters

sample (Sample) – A New Sample!

to_df() → pandas.core.frame.DataFrame

Make a dataframe with sound parameterization flattened out

plot(show=True)

Plot a collection of samples as points, with blue meaning the sample was audible and red meaning inaudible

Examples

from perceptivo.psychophys.oracle import generate_samples

samples = generate_samples(n_samples=1000, scale=10)
samples.plot()

(Source code, png, hires.png, pdf)

Parameters

show (bool) – If True (default), call plt.show()

class perceptivo.types.psychophys.Threshold(frequency: float, threshold: float, confidence: float = 0)

Bases: object

The audible threshold for a particular frequency

Parameters

• frequency (float) – Frequency of threshold in Hz

• threshold (float) – Audible threshold in dbSPL

• confidence (float) – Confidence of threshold, units vary depending on estimation type

frequency: float

threshold: float

confidence: float = 0

class perceptivo.types.psychophys.Audiogram(thresholds: List[perceptivo.types.psychophys.Threshold])

Bases: object

A collection of :class:`.Threshold`s that represent a patient’s audiogram.

Thresholds can be accessed like a dictionary, using frequencies as keys, eg:

>>> agram = Audiogram([Threshold(1000, 10), Threshold(2000, 20)])
>>> agram[1000]
Threshold(frequency=1000, threshold=10, confidence=0)
>>> agram[3000] = Threshold(3000, 30)
>>> agram[3000]
Threshold(frequency=1000, threshold=10, confidence=0)

thresholds: List[perceptivo.types.psychophys.Threshold]

property frequencies: List[float]

List of frequencies in thresholds

to_dict() → Dict[float, float]

Return audiogram thresholds as a {frequency:threshold} dictionary, eg.:

>>> agram = Audiogram([Threshold(1000, 10), Threshold(2000, 20)])
>>> agram.to_dict()
{1000: 10, 2000: 20}

class perceptivo.types.psychophys.Kernel(*, length_scale: Tuple[float, float] = (100.0, 200.0), length_scale_bounds: Tuple[float, float] = (1, 100000.0))

Bases: pydantic.main.BaseModel

Default kernel to use with psychophys.model.Gaussian_Process

Uses a kernel with a short length scale for frequency, but a longer length scale for amplitude, which should be smoother/monotonic where frequency can have an unpredictable shape

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.

length_scale: Tuple[float, float]

length_scale_bounds: Tuple[float, float]

property kernel: sklearn.gaussian_process.kernels.RBF

class Config

Bases: object

arbitrary_types_allowed = True

class perceptivo.types.psychophys.Psychoacoustic_Model(model_type: typing.Literal['Gaussian_Process'] = 'Gaussian_Process', args: typing.Optional[list] = <factory>, kwargs: typing.Optional[typing.Dict[str, perceptivo.types.psychophys.Kernel]] = <factory>)

Bases: object

Parameterization of a psychoacoustic model to use to estimate audiograms and control the presentation of stimuli

model_type: Literal['Gaussian_Process'] = 'Gaussian_Process'

args: Optional[list]

kwargs: Optional[Dict[str, perceptivo.types.psychophys.Kernel]]