Data

A data object contains your entire n-dimensional dataset, including axes, units, channels, and relevant metadata. Once you have a data object, all of the other capabilities of WrightTools are immediately open to you, including processing, fitting, and plotting tools.

Here we highlight some key features of the data object. For a complete list of methods and attributes, see WrightTools.data.Data in the API docs.

Instantiation

From Supported File Types

WrightTools aims to provide user-friendly ways of creating data directly from common spectroscopy file formats. Here are the formats currently supported.

name

description

API

BrunoldrRaman

Files from Brunold lab resonance raman measurements

from_BrunoldrRaman()

Cary

Files from Varian’s Cary® Spectrometers

from_Cary()

COLORS

Files from Control Lots Of Research in Spectroscopy

from_COLORS()

JASCO

Files from JASCO optical spectrometers

from_JASCO()

KENT

Files from “ps control” by Kent Meyer

from_KENT()

Aramis

Horiba Aramis ngc binary files

from_Aramis()

Ocean Optics

.scope files from ocean optics spectrometers

from_ocean_optics()

PyCMDS

Files from PyCMDS

from_PyCMDS()

Shimadzu

Files from Shimadzu UV-VIS spectrophotometers

from_shimadzu()

SPCM

Files from Becker & Hickl spcm software

from_spcm()

Solis

Files from Andor Solis software

from_Solis()

Tensor 27

Files from Bruker Tensor 27 FT-IR

from_Tensor27()

Is your favorite format missing? It’s easy to add—promise! Check out Contributing.

These functions accept both local and remote (http/ftp) files as well as transparent compression (gz/bz2). Compression detection is based on the file name, and file names for remote links are as appears in the link. Many download links (such as those from osf.io or Google drive) do not include extensions in the download link, and thus will cause Warnings/be unable to accept compressed files. This can often be worked around by adding a variable to the end of the url such as https://osf.io/xxxxx/download?fname=file.csv.gz. Google Drive direct download links have the form https://drive.google.com/dc?id=XXXXXXXXXXXXXXXXXXXX (i.e. replace open in the “share” links with dc).

From Bare Arrays

Got bare numpy arrays and dreaming of data? It is possible to create data objects directly, as shown below.

# import
import numpy as np
import WrightTools as wt
# generate arrays for example
def my_resonance(xi, yi, intensity=1, FWHM=500, x0=7000):
    def single(arr, intensity=intensity, FWHM=FWHM, x0=x0):
        return intensity*(0.5*FWHM)**2/((xi-x0)**2+(0.5*FWHM)**2)
    return single(xi) * single(yi)
xi = np.linspace(6000, 8000, 75)[:, None]
yi = np.linspace(6000, 8000, 75)[None, :]
zi = my_resonance(xi, yi)
# package into data object
data = wt.Data(name='example')
data.create_variable(name='w1', units='wn', values=xi)
data.create_variable(name='w2', units='wn', values=yi)
data.create_channel(name='signal', values=zi)
data.transform('w1', 'w2')

Note that NumPy has functions for reading data arrays from text files. Our favorite is genfromtxt. Lean on these functions to read in data from unsuported file formats, then pass in the data as arrays. Of course, if you find yourself processing a lot of data from a particular file format, consider contributing a new from function to WrightTools.

Structure & Attributes

So what is a data object anyway? To put it simply, Data is a collection of WrightTools.data.Axis and WrightTools.data.Channel objects. WrightTools.data.Axis objects are composed of WrightTools.data.Variable objects.

attribute

tuple of…

axes

Axis objects

constants

Constant objects

channels

Channel objects

variables

Variable objects

As mentioned above, the axes and channels within data can be accessed within the data.axes and data.channels lists. Data also supports natural naming, so axis and channel objects can be accessed directly according to their name. The natural syntax is recommended, as it tends to result in more readable code.

>>> data.axis_expressions
('w1', 'w2')
>>> data.w2 == data.axes[1]
True
>>> data.channel_names
('signal', 'pyro1', 'pyro2', 'pyro3')
>>> data.pyro2 == data.channels[2]
True

The order of axes and channels is arbitrary. However many methods within WrightTools operate on the zero-indexed channel by default. For this reason, you can bring your favorite channel to zero-index using bring_to_front().

Variable

The WrightTools.data.Variable class holds key coordinates of the data object. One Variable instance exists for each recorded independent variable. This includes scanned optomechanical hardware, but also still hardware, and other variables like lab time. A typical data object will have many variables (each a multidimensional array). Variables have the following key attributes:

attribute

description

label

LaTeX-formatted label, appropriate for plotting

max()

variable maximum

min()

variable minimum

natural_name

variable name

units

variable units

Axis

The WrightTools.data.Axis class defines the coordinates of a data object. Each Axis contains an expression, which dictates its relationship with one or more variables. Given 5 variables with names ['w1', 'w2', 'wm', 'd1', 'd2'] , example valid expressions include 'w1', 'w1=wm', 'w1+w2', '2*w1', 'd1-d2', and 'wm-w1+w2'. Axes behave like arrays: you can slice into them, view their shape, get a min and max etc. But actually axes do not contain any new array information: they simply refer to the Variable arrays. Axes have the following key attributes:

attribute

description

label()

LaTeX-formatted label, appropriate for plotting

min()

coordinates minimum, in current units

max()

coordinates maximum, in current units

natural_name

axis name

units

current axis units (change with convert())

variables

component variables

expression

expression

Constant

WrightTools.data.Constant objects are a special subclass of Axis objects, which is expected to be a single value. Constant adds the value to to the label attribute, suitable for titles of plots to identify static values associated with the plot. Note that there is nothing enforcing that the value is actually static: constants still have shapes and can be indexed to get the underlying numpy array.

You can control how this label is generated using the attributes format_spec an round_spec. label uses the python builtin format, an thus format_spec is a specification as in the Format Specification Mini-Language. Common examples would be “0.2f” or “0.3e” for decimal representation with two digits past the decimal and engineers notation with 3 digits past the decimal, respectively. round_spec allows you to control the rounding of your number via the builtin round(). For instance, if you want a number rounded to the hundreds position, but represented as an integer, you may use round_spec=-2; format_spec="0.0f".

For example, if you have a constant with value 123.4567 nm, a format_spec of 0.3f, and a round_spec of 2, you will get a label something like '$\\mathsf{\\lambda_{1}\\,=\\,123.460\\,nm}$', which will render as \(\mathsf{\lambda_{1}\,=\,123.460\,nm}\).

An example of using constants/constant labels for plotting can be found in the gallery: Custom Figure.

In addition to the above attributes, constants add:

attribute

description

format_spec

Format specification for how to represent the value, as in format().

round_spec

Specify which digit to round to, as in round()

label

LaTeX formatted label which includes a symbol and the constant value.

value

The mean (ignoring NaNs) of the evaluated expression.

std

The standard deviation of the points used to compute the value.

Channel

The WrightTools.data.Channel class contains the n-dimensional signals. A single data object may contain multiple channels corresponding to different detectors or measurement schemes. Channels have the following key attributes:

attribute

description

label

LaTeX-formatted label, appropriate for plotting

mag()

channel magnitude (furthest deviation from null)

max()

channel maximum

min()

channel minimum

name

channel name

null

channel null (value of zero signal)

signed

flag to indicate if channel is signed

Processing

Units aware & interpolation ready

Experiments are taken over all kinds of dynamic range, with all kinds of units. You might wish to take the difference between a UV-VIS scan taken from 400 to 800 nm, 1 nm steps and a different scan taken from 1.75 to 2.00 eV, 1 meV steps. This can be a huge pain! Even if you converted them to the same unit system, you would still have to deal with the different absolute positions of the two coordinate arrays. map_variable() allows you to easily obtain a data object mapped onto a different set of coordinates.

WrightTools data objects know all about units, and they are able to use interpolation to map between different absolute coordinates. Here we list some of the capabilities that are enabled by this behavior.

method

description

gallery

heal()

use interpolation to guess the value of NaNs within a channel

Heal

join()

join together multiple data objects, accounting for dimensionality and overlap

Join

map_variable()

re-map data coordinates

Map-Variable

Dimensionality without the cursing

Working with multidimensional data can be intimidating. What axis am I looking at again? Where am I in the other axis? Is this slice unusual, or do they all look like that?

WrightTools tries to make multi-dimensional data easy to work with. The following methods deal directly with dimensionality manipulation.

method

description

gallery

chop()

chop data into a list of lower dimensional data

collapse()

destroy one dimension of data using a mathematical strategy

moment()

destroy one dimension of a channel by taking the nth moment

split()

split data at a series of coordinates, without reducing dimensionality

Split

transform()

transform the data on to a new combination of variables as axes

DOVE transform Fringes transform

WrightTools seamlessly handles dimensionality throughout. Artists is one such place where dimensionality is addressed explicitly.

Processing without the pain

There are many common data processing operations in spectroscopy. WrightTools endeavors to make these operations easy. A selection of important methods follows.

method

description

gallery

clip()

clip values outside of a given range (method of Channel)

gradient()

take the derivative along an axis

Gradient

join()

join multiple data objects into one

Join

level()

level the edge of data along a certain axis

Level

smooth()

smooth a channel via convolution with a n-dimensional Kaiser window