lumicks.pylake.channel.Slice¶

class Slice(data_source, labels=None)¶

A lazily evaluated slice of a timeline/HDF5 channel

Users will only ever get these as a result of slicing a timeline/HDF5 channel or slicing another slice (via this class’ __getitem__), i.e. the __init__ method will never be invoked by users.

Parameters:	data_source : Any A slice data source. Can be `Continuous`, `TimeSeries`, ‘TimeTags’, or any other source which conforms to the same interface. labels : Dict[str, str] Plot labels: “x”, “y”, “title”.

__init__(data_source, labels=None)¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(data_source[, labels])	Initialize self.
`downsampled_by`(factor[, reduce])	Return a copy of this slice which is downsampled by `factor`
`plot`(**kwargs)	A simple line plot to visualize the data over time

Attributes

`data`	The primary values of this channel slice
`sample_rate`	The data frequency for continuous data sources or `None` if it’s variable
`timestamps`	Absolute timestamps (since epoch) which correspond to the channel data

__getitem__(item)¶: All indexing is in timestamp units (ns)

downsampled_by(factor, reduce=<function mean>)¶

Return a copy of this slice which is downsampled by factor

Parameters:	factor : int The size and sample rate of the data will be divided by this factor. reduce : callable The `numpy` function which is going to reduce multiple samples into one. The default is `np.mean`, but `np.sum` could also be appropriate for some cases, e.g. photon counts.

plot(**kwargs)¶

A simple line plot to visualize the data over time

Parameters:	**kwargs Forwarded to `matplotlib.pyplot.plot()`.

data¶: The primary values of this channel slice

sample_rate¶: The data frequency for continuous data sources or None if it’s variable

timestamps¶: Absolute timestamps (since epoch) which correspond to the channel data