Skip to content

Data

Package

data

psyphy.data

submodule for handling psychophysical experiment data.

Includes: - dataset: ResponseData, TrialBatch, loaders - transforms: color/model space conversions - io: save/load datasets

Classes:

Name Description
ResponseData

Container for psychophysical trial data.
TrialBatch

Container for a proposed batch of trials

ResponseData 

ResponseData()

Container for psychophysical trial data.

Attributes:

Name Type Description
refs List[Any]

List of reference stimuli.
comparisons List[Any]

List of comparison stimuli.
responses List[int]

List of subject responses (e.g., 0/1 or categorical).

Methods:

Name Description
add_batch

Append responses for a batch of trials.
add_trial

append a single trial.
copy

Create a deep copy of this dataset.
from_arrays

Construct ResponseData from arrays.
merge

Merge another dataset into this one (in-place).
tail

Return last n trials as a new ResponseData.
to_numpy

Return refs, comparisons, responses as numpy arrays.
Source code in src/psyphy/data/dataset.py 
41
42
43
44
def __init__(self) -> None:
    self.refs: list[Any] = []
    self.comparisons: list[Any] = []
    self.responses: list[int] = []

comparisons 

comparisons: list[Any] = []

refs 

refs: list[Any] = []

responses 

responses: list[int] = []

trials 

trials: list[tuple[Any, Any, int]]

Return list of (ref, comparison, response) tuples.

Returns:

Type Description
list[tuple]

Each element is (ref, comparison, resp)

add_batch 

add_batch(
    responses: list[int], trial_batch: TrialBatch
) -> None

Append responses for a batch of trials.

Parameters:

Name Type Description Default
responses List[int]

Responses corresponding to each (ref, comparison) in the trial batch.

 required
trial_batch TrialBatch

The batch of proposed trials.

 required
Source code in src/psyphy/data/dataset.py 
63
64
65
66
67
68
69
70
71
72
73
74
75
def add_batch(self, responses: list[int], trial_batch: TrialBatch) -> None:
    """
    Append responses for a batch of trials.

    Parameters
    ----------
    responses : List[int]
        Responses corresponding to each (ref, comparison) in the trial batch.
    trial_batch : TrialBatch
        The batch of proposed trials.
    """
    for (ref, comparison), resp in zip(trial_batch.stimuli, responses):
        self.add_trial(ref, comparison, resp)

add_trial 

add_trial(ref: Any, comparison: Any, resp: int) -> None

append a single trial.

Parameters:

Name Type Description Default
ref Any

Reference stimulus (numpy array, list, etc.)

 required
comparison Any

Probe stimulus

 required
resp int

Subject response (binary or categorical)

 required
Source code in src/psyphy/data/dataset.py 
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
def add_trial(self, ref: Any, comparison: Any, resp: int) -> None:
    """
    append a single trial.

    Parameters
    ----------
    ref : Any
        Reference stimulus (numpy array, list, etc.)
    comparison : Any
        Probe stimulus
    resp : int
        Subject response (binary or categorical)
    """
    self.refs.append(ref)
    self.comparisons.append(comparison)
    self.responses.append(resp)

copy 

copy() -> ResponseData

Create a deep copy of this dataset.

Returns:

Type Description
ResponseData

New dataset with copied data
Source code in src/psyphy/data/dataset.py 
203
204
205
206
207
208
209
210
211
212
213
214
215
216
def copy(self) -> ResponseData:
    """
    Create a deep copy of this dataset.

    Returns
    -------
    ResponseData
        New dataset with copied data
    """
    new_data = ResponseData()
    new_data.refs = list(self.refs)
    new_data.comparisons = list(self.comparisons)
    new_data.responses = list(self.responses)
    return new_data

from_arrays 

from_arrays(
    X: ndarray | ndarray,
    y: ndarray | ndarray,
    *,
    comparisons: ndarray | ndarray | None = None,
) -> ResponseData

Construct ResponseData from arrays.

Parameters:

Name Type Description Default
X (array, shape(n_trials, 2, input_dim) or (n_trials, input_dim))

Stimuli. If 3D, second axis is [reference, comparison]. If 2D, comparisons must be provided separately.

 required
y (array, shape(n_trials))

Responses

 required
comparisons (array, shape(n_trials, input_dim))

Probe stimuli. Only needed if X is 2D.

 None

Returns:

Type Description
ResponseData

Data container

Examples:

1
2
3
4
>>> # From paired stimuli
>>> X = jnp.array([[[0, 0], [1, 0]], [[1, 1], [2, 1]]])
>>> y = jnp.array([1, 0])
>>> data = ResponseData.from_arrays(X, y)

1
2
3
4
>>> # From separate refs and comparisons
>>> refs = jnp.array([[0, 0], [1, 1]])
>>> comparisons = jnp.array([[1, 0], [2, 1]])
>>> data = ResponseData.from_arrays(refs, y, comparisons=comparisons)
Source code in src/psyphy/data/dataset.py 
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
@classmethod
def from_arrays(
    cls,
    X: jnp.ndarray | np.ndarray,
    y: jnp.ndarray | np.ndarray,
    *,
    comparisons: jnp.ndarray | np.ndarray | None = None,
) -> ResponseData:
    """
    Construct ResponseData from arrays.

    Parameters
    ----------
    X : array, shape (n_trials, 2, input_dim) or (n_trials, input_dim)
        Stimuli. If 3D, second axis is [reference, comparison].
        If 2D, comparisons must be provided separately.
    y : array, shape (n_trials,)
        Responses
    comparisons : array, shape (n_trials, input_dim), optional
        Probe stimuli. Only needed if X is 2D.

    Returns
    -------
    ResponseData
        Data container

    Examples
    --------
    >>> # From paired stimuli
    >>> X = jnp.array([[[0, 0], [1, 0]], [[1, 1], [2, 1]]])
    >>> y = jnp.array([1, 0])
    >>> data = ResponseData.from_arrays(X, y)

    >>> # From separate refs and comparisons
    >>> refs = jnp.array([[0, 0], [1, 1]])
    >>> comparisons = jnp.array([[1, 0], [2, 1]])
    >>> data = ResponseData.from_arrays(refs, y, comparisons=comparisons)
    """
    data = cls()

    X = np.asarray(X)
    y = np.asarray(y)

    if X.ndim == 3:
        # X is (n_trials, 2, input_dim)
        refs = X[:, 0, :]
        comparisons_arr = X[:, 1, :]
    elif X.ndim == 2 and comparisons is not None:
        refs = X
        comparisons_arr = np.asarray(comparisons)
    else:
        raise ValueError(
            "X must be shape (n_trials, 2, input_dim) or "
            "(n_trials, input_dim) with comparisons argument"
        )

    for ref, comparison, response in zip(refs, comparisons_arr, y):
        data.add_trial(ref, comparison, int(response))

    return data

merge 

merge(other: ResponseData) -> None

Merge another dataset into this one (in-place).

Parameters:

Name Type Description Default
other ResponseData

Dataset to merge

 required
Source code in src/psyphy/data/dataset.py 
170
171
172
173
174
175
176
177
178
179
180
181
def merge(self, other: ResponseData) -> None:
    """
    Merge another dataset into this one (in-place).

    Parameters
    ----------
    other : ResponseData
        Dataset to merge
    """
    self.refs.extend(other.refs)
    self.comparisons.extend(other.comparisons)
    self.responses.extend(other.responses)

tail 

tail(n: int) -> ResponseData

Return last n trials as a new ResponseData.

Parameters:

Name Type Description Default
n int

Number of trials to keep

 required

Returns:

Type Description
ResponseData

New dataset with last n trials
Source code in src/psyphy/data/dataset.py 
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
def tail(self, n: int) -> ResponseData:
    """
    Return last n trials as a new ResponseData.

    Parameters
    ----------
    n : int
        Number of trials to keep

    Returns
    -------
    ResponseData
        New dataset with last n trials
    """
    new_data = ResponseData()
    new_data.refs = self.refs[-n:]
    new_data.comparisons = self.comparisons[-n:]
    new_data.responses = self.responses[-n:]
    return new_data

to_numpy 

to_numpy() -> tuple[ndarray, ndarray, ndarray]

Return refs, comparisons, responses as numpy arrays.

Returns:

Name Type Description
refs ndarray
comparisons ndarray
responses ndarray
Source code in src/psyphy/data/dataset.py 
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
def to_numpy(self) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    Return refs, comparisons, responses as numpy arrays.

    Returns
    -------
    refs : np.ndarray
    comparisons : np.ndarray
    responses : np.ndarray
    """
    return (
        np.array(self.refs),
        np.array(self.comparisons),
        np.array(self.responses),
    )

TrialBatch 

TrialBatch(stimuli: list[tuple[Any, Any]])

Container for a proposed batch of trials

Attributes:

Name Type Description
stimuli List[Tuple[Any, Any]]

Each trial is a (reference, comparison) tuple.

Methods:

Name Description
from_stimuli

Construct a TrialBatch from a list of stimuli (ref, comparison) pairs.
Source code in src/psyphy/data/dataset.py 
229
230
def __init__(self, stimuli: list[tuple[Any, Any]]) -> None:
    self.stimuli = list(stimuli)

stimuli 

stimuli = list(stimuli)

from_stimuli 

from_stimuli(pairs: list[tuple[Any, Any]]) -> TrialBatch

Construct a TrialBatch from a list of stimuli (ref, comparison) pairs.

Source code in src/psyphy/data/dataset.py 
232
233
234
235
236
237
@classmethod
def from_stimuli(cls, pairs: list[tuple[Any, Any]]) -> TrialBatch:
    """
    Construct a TrialBatch from a list of stimuli (ref, comparison) pairs.
    """
    return cls(pairs)

Data Containers for Response Data and Proposed Next Trials

dataset

dataset.py

Core data containers for psyphy.

defines: - ResponseData: container for psychophysical trial data - TrialBatch: container for a proposed batch of trials

Notes
  • Data is stored in standard NumPy (mutable!) arrays or Python lists.
  • Use numpy for I/O and analysis.
  • Convert to jax.numpy (jnp) (immutable!) arrays only when passing into WPPM or inference engines that require JAX/Optax.

Classes:

Name Description
ResponseData

Container for psychophysical trial data.
TrialBatch

Container for a proposed batch of trials

ResponseData 

ResponseData()

Container for psychophysical trial data.

Attributes:

Name Type Description
refs List[Any]

List of reference stimuli.
comparisons List[Any]

List of comparison stimuli.
responses List[int]

List of subject responses (e.g., 0/1 or categorical).

Methods:

Name Description
add_batch

Append responses for a batch of trials.
add_trial

append a single trial.
copy

Create a deep copy of this dataset.
from_arrays

Construct ResponseData from arrays.
merge

Merge another dataset into this one (in-place).
tail

Return last n trials as a new ResponseData.
to_numpy

Return refs, comparisons, responses as numpy arrays.
Source code in src/psyphy/data/dataset.py 
41
42
43
44
def __init__(self) -> None:
    self.refs: list[Any] = []
    self.comparisons: list[Any] = []
    self.responses: list[int] = []

comparisons 

comparisons: list[Any] = []

refs 

refs: list[Any] = []

responses 

responses: list[int] = []

trials 

trials: list[tuple[Any, Any, int]]

Return list of (ref, comparison, response) tuples.

Returns:

Type Description
list[tuple]

Each element is (ref, comparison, resp)

add_batch 

add_batch(
    responses: list[int], trial_batch: TrialBatch
) -> None

Append responses for a batch of trials.

Parameters:

Name Type Description Default
responses List[int]

Responses corresponding to each (ref, comparison) in the trial batch.

 required
trial_batch TrialBatch

The batch of proposed trials.

 required
Source code in src/psyphy/data/dataset.py 
63
64
65
66
67
68
69
70
71
72
73
74
75
def add_batch(self, responses: list[int], trial_batch: TrialBatch) -> None:
    """
    Append responses for a batch of trials.

    Parameters
    ----------
    responses : List[int]
        Responses corresponding to each (ref, comparison) in the trial batch.
    trial_batch : TrialBatch
        The batch of proposed trials.
    """
    for (ref, comparison), resp in zip(trial_batch.stimuli, responses):
        self.add_trial(ref, comparison, resp)

add_trial 

add_trial(ref: Any, comparison: Any, resp: int) -> None

append a single trial.

Parameters:

Name Type Description Default
ref Any

Reference stimulus (numpy array, list, etc.)

 required
comparison Any

Probe stimulus

 required
resp int

Subject response (binary or categorical)

 required
Source code in src/psyphy/data/dataset.py 
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
def add_trial(self, ref: Any, comparison: Any, resp: int) -> None:
    """
    append a single trial.

    Parameters
    ----------
    ref : Any
        Reference stimulus (numpy array, list, etc.)
    comparison : Any
        Probe stimulus
    resp : int
        Subject response (binary or categorical)
    """
    self.refs.append(ref)
    self.comparisons.append(comparison)
    self.responses.append(resp)

copy 

copy() -> ResponseData

Create a deep copy of this dataset.

Returns:

Type Description
ResponseData

New dataset with copied data
Source code in src/psyphy/data/dataset.py 
203
204
205
206
207
208
209
210
211
212
213
214
215
216
def copy(self) -> ResponseData:
    """
    Create a deep copy of this dataset.

    Returns
    -------
    ResponseData
        New dataset with copied data
    """
    new_data = ResponseData()
    new_data.refs = list(self.refs)
    new_data.comparisons = list(self.comparisons)
    new_data.responses = list(self.responses)
    return new_data

from_arrays 

from_arrays(
    X: ndarray | ndarray,
    y: ndarray | ndarray,
    *,
    comparisons: ndarray | ndarray | None = None,
) -> ResponseData

Construct ResponseData from arrays.

Parameters:

Name Type Description Default
X (array, shape(n_trials, 2, input_dim) or (n_trials, input_dim))

Stimuli. If 3D, second axis is [reference, comparison]. If 2D, comparisons must be provided separately.

 required
y (array, shape(n_trials))

Responses

 required
comparisons (array, shape(n_trials, input_dim))

Probe stimuli. Only needed if X is 2D.

 None

Returns:

Type Description
ResponseData

Data container

Examples:

1
2
3
4
>>> # From paired stimuli
>>> X = jnp.array([[[0, 0], [1, 0]], [[1, 1], [2, 1]]])
>>> y = jnp.array([1, 0])
>>> data = ResponseData.from_arrays(X, y)

1
2
3
4
>>> # From separate refs and comparisons
>>> refs = jnp.array([[0, 0], [1, 1]])
>>> comparisons = jnp.array([[1, 0], [2, 1]])
>>> data = ResponseData.from_arrays(refs, y, comparisons=comparisons)
Source code in src/psyphy/data/dataset.py 
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
@classmethod
def from_arrays(
    cls,
    X: jnp.ndarray | np.ndarray,
    y: jnp.ndarray | np.ndarray,
    *,
    comparisons: jnp.ndarray | np.ndarray | None = None,
) -> ResponseData:
    """
    Construct ResponseData from arrays.

    Parameters
    ----------
    X : array, shape (n_trials, 2, input_dim) or (n_trials, input_dim)
        Stimuli. If 3D, second axis is [reference, comparison].
        If 2D, comparisons must be provided separately.
    y : array, shape (n_trials,)
        Responses
    comparisons : array, shape (n_trials, input_dim), optional
        Probe stimuli. Only needed if X is 2D.

    Returns
    -------
    ResponseData
        Data container

    Examples
    --------
    >>> # From paired stimuli
    >>> X = jnp.array([[[0, 0], [1, 0]], [[1, 1], [2, 1]]])
    >>> y = jnp.array([1, 0])
    >>> data = ResponseData.from_arrays(X, y)

    >>> # From separate refs and comparisons
    >>> refs = jnp.array([[0, 0], [1, 1]])
    >>> comparisons = jnp.array([[1, 0], [2, 1]])
    >>> data = ResponseData.from_arrays(refs, y, comparisons=comparisons)
    """
    data = cls()

    X = np.asarray(X)
    y = np.asarray(y)

    if X.ndim == 3:
        # X is (n_trials, 2, input_dim)
        refs = X[:, 0, :]
        comparisons_arr = X[:, 1, :]
    elif X.ndim == 2 and comparisons is not None:
        refs = X
        comparisons_arr = np.asarray(comparisons)
    else:
        raise ValueError(
            "X must be shape (n_trials, 2, input_dim) or "
            "(n_trials, input_dim) with comparisons argument"
        )

    for ref, comparison, response in zip(refs, comparisons_arr, y):
        data.add_trial(ref, comparison, int(response))

    return data

merge 

merge(other: ResponseData) -> None

Merge another dataset into this one (in-place).

Parameters:

Name Type Description Default
other ResponseData

Dataset to merge

 required
Source code in src/psyphy/data/dataset.py 
170
171
172
173
174
175
176
177
178
179
180
181
def merge(self, other: ResponseData) -> None:
    """
    Merge another dataset into this one (in-place).

    Parameters
    ----------
    other : ResponseData
        Dataset to merge
    """
    self.refs.extend(other.refs)
    self.comparisons.extend(other.comparisons)
    self.responses.extend(other.responses)

tail 

tail(n: int) -> ResponseData

Return last n trials as a new ResponseData.

Parameters:

Name Type Description Default
n int

Number of trials to keep

 required

Returns:

Type Description
ResponseData

New dataset with last n trials
Source code in src/psyphy/data/dataset.py 
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
def tail(self, n: int) -> ResponseData:
    """
    Return last n trials as a new ResponseData.

    Parameters
    ----------
    n : int
        Number of trials to keep

    Returns
    -------
    ResponseData
        New dataset with last n trials
    """
    new_data = ResponseData()
    new_data.refs = self.refs[-n:]
    new_data.comparisons = self.comparisons[-n:]
    new_data.responses = self.responses[-n:]
    return new_data

to_numpy 

to_numpy() -> tuple[ndarray, ndarray, ndarray]

Return refs, comparisons, responses as numpy arrays.

Returns:

Name Type Description
refs ndarray
comparisons ndarray
responses ndarray
Source code in src/psyphy/data/dataset.py 
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
def to_numpy(self) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
    """
    Return refs, comparisons, responses as numpy arrays.

    Returns
    -------
    refs : np.ndarray
    comparisons : np.ndarray
    responses : np.ndarray
    """
    return (
        np.array(self.refs),
        np.array(self.comparisons),
        np.array(self.responses),
    )

TrialBatch 

TrialBatch(stimuli: list[tuple[Any, Any]])

Container for a proposed batch of trials

Attributes:

Name Type Description
stimuli List[Tuple[Any, Any]]

Each trial is a (reference, comparison) tuple.

Methods:

Name Description
from_stimuli

Construct a TrialBatch from a list of stimuli (ref, comparison) pairs.
Source code in src/psyphy/data/dataset.py 
229
230
def __init__(self, stimuli: list[tuple[Any, Any]]) -> None:
    self.stimuli = list(stimuli)

stimuli 

stimuli = list(stimuli)

from_stimuli 

from_stimuli(pairs: list[tuple[Any, Any]]) -> TrialBatch

Construct a TrialBatch from a list of stimuli (ref, comparison) pairs.

Source code in src/psyphy/data/dataset.py 
232
233
234
235
236
237
@classmethod
def from_stimuli(cls, pairs: list[tuple[Any, Any]]) -> TrialBatch:
    """
    Construct a TrialBatch from a list of stimuli (ref, comparison) pairs.
    """
    return cls(pairs)

Transforms (e.g., from RGB to model space)

transforms

transforms.py

color space transformations.

functions (MVP stubs): - to_model_space(rgb): map RGB stimulus values into model space - to_rgb(model_coords): map from model space back to RGB

Future extensions: - other color spaces

Functions:

Name Description
model_to_stimuli

Map from model space coordinates to RGB values.
stimuli_to_model_space

Map RGB stimulus values to model space coordinates.

Attributes:

Name Type Description
ArrayLike

ArrayLike 

ArrayLike = Union[Sequence[float], ndarray]

model_to_stimuli 

model_to_stimuli(model_coords: ArrayLike) -> ndarray

Map from model space coordinates to RGB values.

Parameters:

Name Type Description Default
model_coords array - like

Model space coordinates.

 required

Returns:

Type Description
ndarray

RGB values (MVP: identical to input).
Source code in src/psyphy/data/transforms.py 
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
def model_to_stimuli(model_coords: ArrayLike) -> np.ndarray:
    """
    Map from model space coordinates to RGB values.

    Parameters
    ----------
    model_coords : array-like
        Model space coordinates.

    Returns
    -------
    np.ndarray
        RGB values (MVP: identical to input).
    """
    return np.array(model_coords)

stimuli_to_model_space 

stimuli_to_model_space(rgb: ArrayLike) -> ndarray

Map RGB stimulus values to model space coordinates.

Parameters:

Name Type Description Default
rgb array - like

RGB values, shape (3,) or similar.

 required

Returns:

Type Description
ndarray

Model-space coordinates (MVP: identical to input).
Source code in src/psyphy/data/transforms.py 
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
def stimuli_to_model_space(rgb: ArrayLike) -> np.ndarray:
    """
    Map RGB stimulus values to model space coordinates.

    Parameters
    ----------
    rgb : array-like
        RGB values, shape (3,) or similar.

    Returns
    -------
    np.ndarray
        Model-space coordinates (MVP: identical to input).
    """
    return np.array(rgb)

I/O

io

io.py

I/O utilities for saving and loading psyphy data.

Supports: - CSV for human-readable trial logs - Pickle (.pkl) for Posterior and ResponseData checkpoints?

Notes
  • Data is stored in NumPy arrays (via ResponseData.to_numpy()).
  • Convert to jax.numpy when passing into models.

Functions:

Name Description
load_posterior

Load a Posterior object from pickle.
load_responses_csv

Load ResponseData from a CSV file.
save_posterior

Save a Posterior object to disk using pickle.
save_responses_csv

Save ResponseData to a CSV file.

Attributes:

Name Type Description
PathLike

PathLike 

PathLike = Union[str, Path]

load_posterior 

load_posterior(path: PathLike) -> object

Load a Posterior object from pickle.

Source code in src/psyphy/data/io.py 
78
79
80
81
82
83
def load_posterior(path: PathLike) -> object:
    """
    Load a Posterior object from pickle.
    """
    with open(path, "rb") as f:
        return pickle.load(f)

load_responses_csv 

load_responses_csv(path: PathLike) -> ResponseData

Load ResponseData from a CSV file.

Parameters:

Name Type Description Default
path str or Path
required

Returns:

Type Description
ResponseData
Source code in src/psyphy/data/io.py 
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
def load_responses_csv(path: PathLike) -> ResponseData:
    """
    Load ResponseData from a CSV file.

    Parameters
    ----------
    path : str or Path

    Returns
    -------
    ResponseData
    """
    data = ResponseData()
    with open(path) as f:
        reader = csv.DictReader(f)
        for row in reader:
            ref = ast.literal_eval(row["ref"])
            probe = ast.literal_eval(row["probe"])
            resp = int(row["response"])
            data.add_trial(ref, probe, resp)
    return data

save_posterior 

save_posterior(posterior: object, path: PathLike) -> None

Save a Posterior object to disk using pickle.

Source code in src/psyphy/data/io.py 
70
71
72
73
74
75
def save_posterior(posterior: object, path: PathLike) -> None:
    """
    Save a Posterior object to disk using pickle.
    """
    with open(path, "wb") as f:
        pickle.dump(posterior, f)

save_responses_csv 

save_responses_csv(
    data: ResponseData, path: PathLike
) -> None

Save ResponseData to a CSV file.

Parameters:

Name Type Description Default
data ResponseData
required
path str or Path
required
Source code in src/psyphy/data/io.py 
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
def save_responses_csv(data: ResponseData, path: PathLike) -> None:
    """
    Save ResponseData to a CSV file.

    Parameters
    ----------
    data : ResponseData
    path : str or Path
    """
    refs, probes, resps = data.to_numpy()
    with open(path, "w", newline="") as f:
        writer = csv.writer(f)
        writer.writerow(["ref", "probe", "response"])
        for r, p, y in zip(refs, probes, resps):
            writer.writerow([r.tolist(), p.tolist(), int(y)])