Basic transformations

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

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

SpectroChemPy's API - v.0.7.1 © Copyright 2014-2025 - 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-az1774-299
created	2025-02-25 08:04:09+00:00
description	Omnic title: NH4Y-activation.SPG Omnic filename: /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg
history	2025-02-25 08:04:09+00:00> Imported from spg file /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg. 2025-02-25 08:04:09+00:00> 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
t_dataset = dataset.T
t_dataset

name	nh4y-activation
author	runner@fv-az1774-299
created	2025-02-25 08:04:09+00:00
description	Omnic title: NH4Y-activation.SPG Omnic filename: /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg
history	2025-02-25 08:04:09+00:00> Imported from spg file /home/runner/.spectrochempy/testdata/irdata/nh4y-activation.spg. 2025-02-25 08:04:09+00:00> Sorted by date 2025-02-25 08:04:11+00:00> 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:

_ = t_dataset.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)
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 for more details on these units operations