WrightTools.data.Channel

class WrightTools.data.Channel(parent, id, **kwargs)

Channel.

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

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

__init__(parent, id, *[, units, null, ...])	Construct a channel object.
argmax()	Index of the maximum, ignorning nans.
argmin()	Index of the minimum, ignoring nans.
asstr([encoding, errors])	Get a wrapper to read string data as Python strings:
astype(dtype)	Get a wrapper allowing you to perform reads to a different destination type, e.g.:
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.
fields(names, *[, _prior_dtype])	Get a wrapper to read a subset of fields from a compound data type:
flush()	Flush the dataset data and metadata to the file.
iter_chunks([sel])	Return chunk iterator.
len()	The size of the first axis.
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).
make_scale([name])	Make this dataset an HDF5 dimension scale.
max()	Maximum, ignorning nans.
min()	Minimum, ignoring nans.
normalize([mag])	Normalize a Channel, set null to 0 and the mag to given value.
read_direct(dest[, source_sel, dest_sel])	Read data directly from HDF5 into an existing NumPy array.
refresh()	Refresh the dataset metadata by reloading from the file.
resize(size[, axis])	Resize the dataset, or the specified axis.
slices()	Returns a generator yielding tuple of slice objects.
symmetric_root([root])
trim(neighborhood[, method, factor, ...])	Remove outliers from the dataset.
virtual_sources()	Get a list of the data mappings for a virtual dataset
write_direct(source[, source_sel, dest_sel])	Write data directly to HDF5 from a NumPy array.

Attributes

attrs	Attributes attached to this object
chunks	Dataset chunks (or None)
class_name
compression	Compression strategy (or None)
compression_opts	Compression setting.
dims	Access dimension scales attached to this dataset.
dtype	Numpy dtype representing the datatype
external	External file settings.
file	Return a File instance associated with this object
fillvalue	Fill value for this dataset (0 by default)
fletcher32	Fletcher32 filter is present (T/F)
full
fullpath	file and internal structure.
id	Low-level identifier appropriate for this object
is_scale	Return True if this dataset is also a dimension scale.
is_virtual	Check if this is a virtual dataset
major_extent	Maximum deviation from null.
maxshape	Shape up to which this dataset can be resized.
minor_extent	Minimum deviation from null.
name	Return the full name of this object.
natural_name	Natural name of the dataset.
nbytes	Numpy-style attribute giving the raw dataset size as the number of bytes
ndim	Numpy-style attribute giving the number of dimensions
null
parent	Parent.
points	Squeezed array.
ref	An (opaque) HDF5 reference to this object
regionref	Create a region reference (Datasets only).
scaleoffset	Scale/offset filter settings.
shape	Numpy-style shape tuple giving dataset dimensions
shuffle	Shuffle filter present (T/F)
signed
size	Numpy-style attribute giving the total dataset size
units	Units.