||Merge time axes|
||A stimulus is comprised of a labeled 2D NumPy array that contains the data, where the rows denote electrodes and the columns denote points in time.|
Stimulus(source, electrodes=None, time=None, metadata=None, compress=False)¶
A stimulus is comprised of a labeled 2D NumPy array that contains the data, where the rows denote electrodes and the columns denote points in time. A stimulus can be created from a variety of source types (e.g., scalars, lists, NumPy arrays, and dictionaries).
New in version 0.6.
- source (source type) –
A valid source type is one of the following:
- Scalar value: interpreted as the current amplitude delivered to a single electrode (no time component).
- NumPy array:
- Nx1 array: interpreted as N current amplitudes delivered to N electrodes (no time component).
- NxM array: interpreted as N electrodes each receiving M current amplitudes in time.
In addition, you can also pass a collection of source types. Each element must be a valid source type for a single electrode (e.g., scalar, 1-D array,
- List or tuple: List elements will be assigned to electrodes in order.
- Dictionary: Dictionary keys are used to address electrodes by name.
- electrodes (int, string or list thereof; optional) –
Optionally, you can provide your own electrode names. If none are given, electrode names will be extracted from the source type (e.g., the keys from a dictionary). If a scalar or NumPy array is passed, electrode names will be numbered 0..N.
The number of electrode names provided must match the number of electrodes extracted from the source type (i.e., N).
- time (int, float or list thereof; optional) –
Optionally, you can provide the time points of the source data. If none are given, time steps will be numbered 0..M.
The number of time points provided must match the number of time points extracted from the source type (i.e., M). Stimuli created from scalars or 1-D NumPy arrays will have no time componenet, in which case you cannot provide your own time points.
- metadata (dict, optional) – Additional stimulus metadata can be stored in a dictionary.
- compress (bool, optional) –
If True, will compress the source data in two ways:
- Remove electrodes with all-zero activation.
- Retain only the time points at which the stimulus changes.
For example, in a pulse train, only the signal edges are saved. This drastically reduces the memory footprint of the stimulus.
- Depending on the source type, a stimulus might have a time component or not (e.g., scalars: time=None).
- You can access the stimulus applied to electrode
tby directly indexing into
Stimulus[e, t]. In this case,
tis not a column index but a time point.
- If the time point is not explicitly stored in the
datacontainer, its value will be automatically interpolated from neighboring values.
- If a requested time point lies outside the range of stored data, the value of its closest end point will be returned.
Stimulate a single electrode with -13uA:
>>> from pulse2percept.stimuli import Stimulus >>> stim = Stimulus(-13)
Stimulate ten electrodes with 0uA:
>>> from pulse2percept.stimuli import Stimulus >>> stim = Stimulus(np.zeros(10))
Provide new electrode names for an existing Stimulus object:
>>> from pulse2percept.stimuli import Stimulus >>> old_stim = Stimulus([3, 5]) >>> new_stim = Stimulus(old_stim, electrodes=['new0', 'new1'])
Interpolate the stimulus value at some point in time. Here, the stimulus is a single-electrode ramp stimulus (stimulus value == point in time):
>>> from pulse2percept.stimuli import Stimulus >>> stim = Stimulus(np.arange(10).reshape((1, -1))) >>> stim[:, 3.45] # doctest: +ELLIPSIS 3.45...
Append another stimulus
This method appends another stimulus (with matching electrodes) in time. The combined stimulus duration will be the sum of the two individual stimuli.
New in version 0.7.
Parameters: other (
Stimulus) – Another stimulus with matching electrodes.
Returns: comb (
Stimulus) – A combined stimulus with the same number of electrodes and new stimulus duration equal to the sum of the two individual stimuli.
Stimulus data container A 2-D NumPy array that contains the stimulus data, where the rows denote electrodes and the columns denote points in time.
Sampling time step (ms)
Defines the duration of the signal edge transitions.
New in version 0.7.
Stimulus duration (ms)
Electrode names A list of electrode names, corresponding to the rows in the data container.
Flag indicating whether the stimulus is charge-balanced
A stimulus with a time component is considered charge-balanced if its net current is smaller than 10 pico Amps. For the whole stimulus to be charge-balanced, every electrode must be charge-balanced as well.
Flag indicating whether the stimulus has been compressed
plot(electrodes=None, time=None, fmt='k-', ax=None)¶
Plot the stimulus
New in version 0.7.
- electrodes (int, string, or list thereof; optional, default: None) – The electrodes for which to plot the stimulus. If None, all electrodes are plotted.
- time ((t_min, t_max) tuple, slice, or list of exact time points) – The time points at which to plot the stimulus. Specify a range of time points with a tuple or a slice, or specify the exact time points to interpolate. If None, all time points are plotted.
- fmt (str, optional, default: 'k-') – A Matplotlib format string; e.g., ‘ro’ for red circles.
- ax (matplotlib.axes.Axes or list thereof; optional, default: None) – A Matplotlib Axes object or a list thereof (one per electrode to plot). If None, a new Axes object will be created.
axes (matplotlib.axes.Axes or np.ndarray of them) – Returns one matplotlib.axes.Axes per electrode
Removes the stimulus of a certain electrode or list of electrodes.
New in version 0.8.
Parameters: electrodes (int, string, or list of int/str) – The item(s) to remove from the stimulus. Can either be an electrode index, electrode name, or a list thereof.
Data container shape
Time steps A list of time steps, corresponding to the columns in the data container.
- source (source type) –
merge_time_axes(data, time, merge_tolerance=1e-06)¶
Merge time axes
When a collection of source types is passed, it is possible that they have different time axes (e.g., different time steps, or a different stimulus duration). In this case, we need to merge all time axes into a single, coherent one. This is expensive, because of interpolation.
- data (list) – List of numpy.ndarray’s containing data points associated with time axes.
- time (list) – List of numpy.ndarray’s containing time points to merge
- merge_tolerance (float) – Float representing the tolerance used when collecting unique time points from the time axes.
- Returns – Tuple of: list of new data points (linearly interpolated from merged time axis), list of new merged time axis.
- ------- –