Warning

You are reading the documentation related to the development version. Go here if you are looking for the documentation of the stable release.

Basic transformations

Let’s show some SpectroChemPy features on a group of IR spectra

import spectrochempy as scp
from spectrochempy import MASKED, DimensionalityError, error_

SpectroChemPy's API - v.0.6.9.dev9 © Copyright 2014-2024 - A.Travert & C.Fernandez @ LCS

dataset = scp.read_omnic("irdata/nh4y-activation.spg")
dataset.y -= dataset.y[0]
dataset.y.title = "time"
dataset

name	nh4y-activation
author	runner@fv-az1501-19
created	2024-04-28 03:09:27+02:00
description	Omnic title: NH4Y-activation.SPG Omnic filename: /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg
history	2024-04-28 03:09:27+02:00> Imported from spg file /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg. 2024-04-28 03:09:27+02:00> Sorted by date
DATA
title	absorbance
values	[[ 2.057 2.061 ... 2.013 2.012] [ 2.033 2.037 ... 1.913 1.911] ... [ 1.794 1.791 ... 1.198 1.198] [ 1.816 1.815 ... 1.24 1.238]] a.u.
shape	(y:55, x:5549)
DIMENSION `x`
size	5549
title	wavenumbers
coordinates	[ 6000 5999 ... 650.9 649.9] cm⁻¹
DIMENSION `y`
size	55
title	time
coordinates	[ 0 600 ... 3.24e+04 3.3e+04] s
labels	[[ 2016-07-06 19:03:14+00:00 2016-07-06 19:13:14+00:00 ... 2016-07-07 04:03:17+00:00 2016-07-07 04:13:17+00:00] [ vz0466.spa, Wed Jul 06 21:00:38 2016 (GMT+02:00) vz0467.spa, Wed Jul 06 21:10:38 2016 (GMT+02:00) ... vz0520.spa, Thu Jul 07 06:00:41 2016 (GMT+02:00) vz0521.spa, Thu Jul 07 06:10:41 2016 (GMT+02:00)]]

prefs = dataset.preferences
prefs.figure.figsize = (6, 3)
prefs.colormap = "Dark2"
prefs.colorbar = True
ax = dataset.plot()

Masking data

if we try to get for example the maximum of this dataset, we face a problem due to the saturation around 1100 cm\(^{-1}\).

dataset.max()

6.0 a.u.

One way is to apply the max function to only a part of the spectrum (using slicing). Another way is to mask the undesired data.

Masking values in this case is straightforward. Just set a value masked or True for those data you want to mask.

dataset[:, 1290.0:890.0] = MASKED
# note that we specify floating values in order to sect wavenumbers, not index.

Here is a display the figure with the new mask

_ = dataset.plot_stack()

Now the max function return the maximum in the unmasked region, which is exactly what we wanted.

dataset.max()

3.8080601692199707 a.u.

To clear this mask, we can simply do:

dataset.remove_masks()
_ = dataset.plot()

Transposition

Dataset can be transposed

dataset[:, 1290.0:890.0] = MASKED  # we mask the unwanted columns
datasetT = dataset.T
datasetT

name	nh4y-activation
author	runner@fv-az1501-19
created	2024-04-28 03:09:27+02:00
description	Omnic title: NH4Y-activation.SPG Omnic filename: /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg
history	2024-04-28 03:09:27+02:00> Imported from spg file /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg. 2024-04-28 03:09:27+02:00> Sorted by date 2024-04-28 03:09:29+02:00> Data transposed
DATA
title	absorbance
values	[[ 2.057 2.033 ... 1.794 1.816] [ 2.061 2.037 ... 1.791 1.815] ... [ 2.013 1.913 ... 1.198 1.24] [ 2.012 1.911 ... 1.198 1.238]] a.u.
shape	(x:5549, y:55)
DIMENSION `x`
size	5549
title	wavenumbers
coordinates	[ 6000 5999 ... 650.9 649.9] cm⁻¹
DIMENSION `y`
size	55
title	time
coordinates	[ 0 600 ... 3.24e+04 3.3e+04] s
labels	[[ 2016-07-06 19:03:14+00:00 2016-07-06 19:13:14+00:00 ... 2016-07-07 04:03:17+00:00 2016-07-07 04:13:17+00:00] [ vz0466.spa, Wed Jul 06 21:00:38 2016 (GMT+02:00) vz0467.spa, Wed Jul 06 21:10:38 2016 (GMT+02:00) ... vz0520.spa, Thu Jul 07 06:00:41 2016 (GMT+02:00) vz0521.spa, Thu Jul 07 06:10:41 2016 (GMT+02:00)]]

As it can be observed the dimension xand yhave been exchanged, e.g. the original shape was (x: 5549, y: 55), and after transposition it is (y:55, x:5549). (the dimension names stay the same, but the index of the corresponding axis are exchanged).

Let’s visualize the result:

_ = datasetT.plot()

Changing units

Units of the data and coordinates can be changed, but only towards compatible units. For instance, data are in absorbance units, which are dimensionless (a.u). So a dimensionless units such as radian is allowed, even if in this case it makes very little sense.

dataset.units = "radian"

_ = dataset.plot()

Trying to change it in ‘meter’ for instance, will generate an error!

try:
    dataset.to("meter")
except DimensionalityError as e:
    error_(DimensionalityError, e)

 ERROR | DimensionalityError: Cannot convert from 'radian' (dimensionless) to 'meter' ([length])

If this is for some reasons something you want to do, you must for the change:

d = dataset.to("meter", force=True)
print(d.units)

m

When units are compatible there is no problem to modify it. For instance, we can change the y dimension units ( Time) to hours. Her we use the inplace transformation ito .

dataset.y.ito("hours")
_ = dataset.plot()

See Units and Quantities for more details on these units operations