WrightTools.data.Channel

class WrightTools.data.Channel(parent, id, *, units=None, null=None, signed=None, label=None, label_seed=None, **kwargs)[source]

Bases: WrightTools._dataset.Dataset

Channel.

__init__(parent, id, *, units=None, null=None, signed=None, label=None, label_seed=None, **kwargs)[source]

Construct a channel object.

Parameters
  • values (array-like) – Values.

  • name (string) – Channel name.

  • units (string (optional)) – Channel units. Default is None.

  • null (number (optional)) – Channel null. Default is None (0).

  • signed (booelan (optional)) – Channel signed flag. Default is None (guess).

  • label (string.) – Label. Default is None.

  • label_seed (list of strings) – Label seed. Default is None.

  • **kwargs – Additional keyword arguments are added to the attrs dictionary and to the natural namespace of the object (if possible).

Methods

argmax()

Index of the maximum, ignorning nans.

argmin()

Index of the minimum, ignoring nans.

chunkwise(func, *args, **kwargs)

Execute a function for each chunk in the dataset.

clip([min, max, replace])

Clip values outside of a defined range.

convert(destination_units)

Convert units.

log([base, floor])

Take the log of the entire dataset.

log10([floor])

Take the log base 10 of the entire dataset.

log2([floor])

Take the log base 2 of the entire dataset.

mag()

Channel magnitude (maximum deviation from null).

max()

Maximum, ignorning nans.

min()

Minimum, ignoring nans.

normalize([mag])

Normalize a Channel, set null to 0 and the mag to given value.

slices()

Returns a generator yielding tuple of slice objects.

symmetric_root([root])

trim(neighborhood[, method, factor, …])

Remove outliers from the dataset.

Attributes

attrs

Attributes attached to this object

class_name

dtype

Numpy dtype representing the datatype

file

Return a File instance associated with this object

fillvalue

Fill value for this dataset (0 by default)

flush

Flush the dataset data and metadata to the file.

full

fullpath

file and internal structure.

major_extent

Maximum deviation from null.

minor_extent

Minimum deviation from null.

name

Return the full name of this object.

natural_name

Natural name of the dataset.

ndim

Numpy-style attribute giving the number of dimensions

null

parent

Parent.

points

Squeezed array.

shape

Numpy-style shape tuple giving dataset dimensions

signed

size

Numpy-style attribute giving the total dataset size

units

Units.

value

Alias for dataset[()]