Import Data in SpectroChemPy

This tutorial shows how to import data in SpectroChemPy (SCPy) .

First, let’s import spectrochempy as scp in the current namespace, so that all spectrochempy commands will be called as scp.method(<method parameters>) .

[1]:

import spectrochempy as scp

Generic read command

To read a file containing spectrocopic data or similar, the read method can be used. This method will try to guess the file format based on the file extension.

[2]:

X = scp.read("wodger.spg")

The above command will extract the data from the file wodger.spg and store it in a NDDataset object named X. To display information about the dataset, simply type X in a cell and run it.

[3]:

[3]:

NDDataset [wodger] — float64, shape: (y:2, x:5549), a.u.

name

:

wodger

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:00+00:00

description

:

Omnic title: wodger.spg
Omnic filename: /home/runner/work/spectrochempy/spectrochempy/docs/sources/userguide/importexport/wodger.spg

history

:

2026-07-30 19:08:00+00:00> Imported from spg file /home/runner/work/spectrochempy/spectrochempy/docs/sources/userguide/importexport/wodger.spg.
2026-07-30 19:08:00+00:00> Sorted by date

Data

title

:

absorbance

values

:

[[ 2.005 2.003 ... 1.826 1.831]
[ 1.983 1.984 ... 1.698 1.704]] a.u.

shape

:

(y:2, x:5549)

Dimension `x`

size

:

5549

title

:

wavenumbers

coordinates

:

[ 6000 5999 ... 650.9 649.9] cm⁻¹

Dimension `y`

size

:

2

title

:

acquisition timestamp (GMT)

coordinates

:

[1.468e+09 1.468e+09] s

labels

:

[[ vz0470.spa, Wed Jul 06 21:40:37 2016 (GMT+02:00) 2016-07-06 19:43:13+00:00]
[ vz0468.spa, Wed Jul 06 21:20:38 2016 (GMT+02:00) 2016-07-06 19:23:14+00:00]]

In this case, the data were in an OMNIC file format, and the read method guessed it correctly using the file name extension. The read method can also read other file formats, such as OPUS, JCAMP-DX, CSV, MATLAB, TOPSPIN, etc. or even a directory.

How the generic reader works

Under the hood, scp.read(...) acts as a dispatcher that inspects the file extension (or the optional protocol= keyword) and delegates the actual reading to the appropriate format-specific implementation:

scp.read(path)
        │
        ▼
format detection
        │
        ▼
appropriate namespace reader
        │
        ▼
scp.omnic.read(path)
scp.opus.read(path)
scp.jcamp.read(path)
...

The generic reader is convenient because you do not need to remember which namespace or alias to use. However, the namespace API (scp.omnic.read, scp.opus.read, scp.jcamp.read, …) is useful when you want to make the format explicit in your code, or when the automatic detection is ambiguous.

You can also force a protocol manually:

[4]:

X = scp.read("wodger.spg", protocol="omnic")
X

[4]:

NDDataset [wodger] — float64, shape: (y:2, x:5549), a.u.

name

:

wodger

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:00+00:00

description

:

Omnic title: wodger.spg
Omnic filename: /home/runner/work/spectrochempy/spectrochempy/docs/sources/userguide/importexport/wodger.spg

history

:

2026-07-30 19:08:00+00:00> Imported from spg file /home/runner/work/spectrochempy/spectrochempy/docs/sources/userguide/importexport/wodger.spg.
2026-07-30 19:08:00+00:00> Sorted by date

Data

title

:

absorbance

values

:

[[ 2.005 2.003 ... 1.826 1.831]
[ 1.983 1.984 ... 1.698 1.704]] a.u.

shape

:

(y:2, x:5549)

Dimension `x`

size

:

5549

title

:

wavenumbers

coordinates

:

[ 6000 5999 ... 650.9 649.9] cm⁻¹

Dimension `y`

size

:

2

title

:

acquisition timestamp (GMT)

coordinates

:

[1.468e+09 1.468e+09] s

labels

:

[[ vz0470.spa, Wed Jul 06 21:40:37 2016 (GMT+02:00) 2016-07-06 19:43:13+00:00]
[ vz0468.spa, Wed Jul 06 21:20:38 2016 (GMT+02:00) 2016-07-06 19:23:14+00:00]]

Using a specific reader

Instead of using the generic read method, you can also use a specific reader, such as read_omnic, read_opus, read_csv, read_jcamp, etc. These methods are more specific and will only read the file format they are. For example, read_omnic will only read OMNIC files.

The following table lists the available file readers in SCPy along with the corresponding file formats and extensions they support:

Reader	File Formats	Extensions
read_omnic,read_spa,read_spg,read_srs	Thermo Scientific/Nicolet OMNIC files	.spa, .spg, .srs
read_opus	Bruker OPUS files	.0, .1, .000, …
read_csv	Comma-Separated Values (CSV) files	.csv
read_jcamp, read_dx	JCAMP-DX spectral data files	.dx, .jdx
read_matlab,read_mat	MATLAB files	.mat, .dso
scp.nmr.read	Bruker TopSpin NMR files (requires spectrochempy-nmr plugin)	fid, ser, 1r, 1i, 2rr…
read_labspec	LABSPEC6 spectral data files	.txt
read_wire,read_wdf	Renishaw Wire files	.wdf
read_scp	SpectroChemPy-specific files	.scp
read_soc,read_ddr,read_hdr,read_sdr	Surface Optics Corporation files	.ddr, .hdr, .sdr
read_galactic	Galactic spectral files	.spc
read_quadera	Pfeiffer Vacuum QUADERA mass spectrometer files	.txt
scp.perkinelmer.read	PerkinElmer SP files (requires spectrochempy-perkinelmer plugin)	.sp
read	Generic reader (automatically detects format)
read_dir	Reads all supported files in a directory
read_zip	Reads files from a ZIP archive	.zip
scp.carroucell.read	Reads files from a carroucell experiment directory

The read_dir function scans a directory and reads all supported files, returning a list of NDDataset objects.

Historical .scp and .pscp files are trusted native persistence archives. If such a file requires legacy pickle-based decoding, load it explicitly with:

scp.read("example.scp", allow_unsafe_legacy=True)

For a short transition period, the contrast with newly written native files is worth keeping explicit:

# Newly written native file:
# ds = scp.read("new.scp")

# Portable NetCDF round-trip for NDDataset:
# ds.to_netcdf("new.nc")
# ds2 = scp.NDDataset.from_netcdf("new.nc")

# Historical trusted legacy file only:
# ds = scp.read("old.scp", allow_unsafe_legacy=True)

Only enable allow_unsafe_legacy=True for files from known and trusted sources.

NetCDF is a portable persistence path built on the canonical NDDataset ↔ xarray.Dataset mapping. It currently covers NDDataset only; full CoordSet fidelity and Project round-trips are not guaranteed by the current portable subset.

MATLAB export is available as a minimal exchange writer through write_matlab() / write_mat() for simple numeric NDDataset objects. It is not SpectroChemPy native persistence and does not aim at full round-trip fidelity. For structured SpectroChemPy storage, prefer native safe persistence or the portable xarray / NetCDF path.

Other reader functions return either a single NDDataset or multiple NDDataset objects, depending on the file type and content.

Further details on specific cases are provided below, especially in the section on reading directories.

Using namespace APIs

Core I/O domains also expose a namespace-style API for explicit domain-qualified access:

scp.omnic.read("wodger.spg")
scp.opus.read("irdata/OPUS/test.0000")
scp.csv.read("irdata/csv/iris.csv")
scp.jcamp.read("irdata/jcamp/nh4y-activation.jdx")

These are equivalent to their top-level aliases (read_omnic, read_opus, read_csv, read_jcamp, etc.) and are provided for API clarity and future-proofing. The generic scp.read(...) entry point remains the recommended default.

Using relative or absolute pathnames

In the above examples, the file wodger.spg was read from the current working directory.

If the file is located in another directory, the full path to the file can be provided. For example:

X = scp.read('/users/Brian/s/Life/wodger.spg')

or, for Windows:

X = scp.read(r'C:\\users\\Brian\\s\\Life\\wodger.spg')

Notes:

The path separator is a backslash \\ on Windows, but in many contexts, backslash is also used as an escape character to represent non-printable characters. To avoid problems, either it has to be escaped itself, a double backslash \\, or one can also use raw string literals to represent Windows paths. These are string literals that have an r prepended to them. In raw string literals, \\ represents a literal backslash, e.g. r'C:\\users\\Brian'.
In python, the slash / is used as the path separator in all systems (Windows, Linux, OSX, …). So it can be used in all cases. For example:
```
X = scp.read('C:/users/Brian/s/Life/wodger.spg')
```
The use of relative pathnames is a good practice. SpectroChemPy readers use relative paths. If the given path is not absolute, then SpectroChemPy will search relative to the current directory or to a directory specified using the directory keyword.

For example:
```
X = scp.read('wodger.spg', directory='C:/users/Brian/s/Life')
X = scp.read('Life/wodger.spg', directory='C:\\users\\Brian\\s')
```
The os or pathlib modules can be used to work with pathnames. See the “Use os or pathlib packages” section below.
The preferences.datadir variable can be used to set a default directory where to look for data. See the “Another default search directory: datadir” section below.

Good practices

Use relative paths

As path are system dependent, it is a good practice to use relative pathnames in scripts and notebooks.

If, for instance, Brian has a project organised in a folder (s) with a directory dedicated to input data (Life) and a notebook for preprocessing (welease.ipynb) as illustrated below:

C:\users
|    +-- Brian
|    |    +-- s
|    |    |   +-- Life
|    |    |   |   +-- wodger.spg
|    |    |   +-- welease.ipynb

Then running this project in John’s Linux computer (e.g. in /home/john/s_copy ) will certainly result in execution errors if absolute paths are used in the notebook:

OSError: Can't find this filename C:\\users\\Brian\\s\\life\\wodger.spg

Fortunately, SpectroChemPy readers use relative paths. If the given path is not absolute, then SpectroChemPy will search in the current directory. Hence, the opening of the spg file from scripts in welease.ipynb can be made by the command:

X = scp.read('Life/wodger.spg')

or:

X = scp.read('wodger.spg', directory='Life')

Use os or pathlib packages

In python, working with pathnames is classically done with dedicated modules such as os or pathlib python modules. With os we mention the following methods that can be particularly useful:

import os
os.getcwd()              # returns the absolute path of the current working directory
os.path.expanduser("~")  # returns the home directory of the user
os.path.join('path1', 'path2', 'path3', ...)

Using Pathlib is even simpler:

from pathlib import Path
Path.cwd()               # returns the absolute path of the current working directory
Path.home()              # returns the home directory of the user
Path('path1') / 'path2' / 'path3' / '...'

The interested readers will find more details on the use of these modules here:

os
pathlib

Another default search directory: datadir

Spectrochempy also comes with the definition of a second default directory path where to look at the data: the datadir directory. It is defined in the variable preferences.datadir which is imported at the same time as spectrochempy. By default, datadir points in the $HOME/.spectrochempy/testdata directory.

[5]:

DATADIR = scp.preferences.datadir
DATADIR

[5]:

PosixPath('/home/runner/.spectrochempy/testdata')

DATADIR is already a pathlib object and so can be used easily

[6]:

scp.read_omnic(DATADIR / "wodger.spg")

[6]:

NDDataset [wodger] — float64, shape: (y:2, x:5549), a.u.

name

:

wodger

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:00+00:00

description

:

Omnic title: wodger.spg
Omnic filename: /home/runner/.spectrochempy/testdata/wodger.spg

history

:

2026-07-30 19:08:00+00:00> Imported from spg file /home/runner/.spectrochempy/testdata/wodger.spg.
2026-07-30 19:08:00+00:00> Sorted by date

Data

title

:

absorbance

values

:

[[ 2.005 2.003 ... 1.826 1.831]
[ 1.983 1.984 ... 1.698 1.704]] a.u.

shape

:

(y:2, x:5549)

Dimension `x`

size

:

5549

title

:

wavenumbers

coordinates

:

[ 6000 5999 ... 650.9 649.9] cm⁻¹

Dimension `y`

size

:

2

title

:

acquisition timestamp (GMT)

coordinates

:

[1.468e+09 1.468e+09] s

labels

:

[[ vz0470.spa, Wed Jul 06 21:40:37 2016 (GMT+02:00) 2016-07-06 19:43:13+00:00]
[ vz0468.spa, Wed Jul 06 21:20:38 2016 (GMT+02:00) 2016-07-06 19:23:14+00:00]]

It can be set to another pathname permanently (i.e., even after computer restart) by a new assignment:

scp.preferences.datadir = 'C:/users/Brian/s/Life'

This will change the default value in the SCPy preference file located in the hidden folder .spectrochempy/ at the root of the user home directory.

Finally, by default, the import functions used in Spectrochempy will search the data files using this order of precedence:

try absolute path
try in current working directory
try in datadir
if none of these works: generate an OSError (file or directory not found)

Reading directories

The read_dir function is designed to read an entire directory, create NDDatasets for each file, and finally merge all compatible datasets. Let’s see an example:

Here is a list of the files present in DATADIR/irdata/subdir/.

[7]:

folder = DATADIR / "irdata" / "subdir"
[str(item.relative_to(DATADIR)) for item in folder.glob("*.*")]

[7]:

['irdata/subdir/7_CZ0-100_Pd_102.SPA',
 'irdata/subdir/TGAIR-unreadable.srs',
 'irdata/subdir/7_CZ0-100_Pd_103.SPA',
 'irdata/subdir/7_CZ0-100_Pd_104.SPA',
 'irdata/subdir/7_CZ0-100_Pd_101.SPA']

Now read all files in the DATADIR/irdata/subdir/ directory (i.e., four .spa files and one .srs file). Any file in an unknown format will be ignored silently:

[8]:

scp.read_dir(folder)

[8]:

List (len=2, type=NDDataset)

0: NDDataset [dd_6.6_19039_538] — float64, shape: (y:335, x:1868), a.u.

name

:

dd_6.6_19039_538

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:00+00:00

description

:

Concatenation of 1 datasets:
( dd_6.6_19039_538 )

history

:

2026-07-30 19:08:00+00:00> Created by concatenate
2026-07-30 19:08:00+00:00> Merged from several files

Data

title

:

absorbance

values

:

...

[[-0.007524 -0.0003661 ... 8.291e-05 9.239e-05]
[-0.009306 -0.002252 ... 0.0001051 0.000107]
...
[ 0.02474 0.02814 ... 0.002962 0.002967]
[ 0.02663 0.02899 ... 0.002907 0.002916]] a.u.

shape

:

(y:335, x:1868)

Dimension `x`

size

:

1868

title

:

wavenumbers

coordinates

:

[ 4000 3998 ... 401.1 399.2] cm⁻¹

Dimension `y`

size

:

335

title

:

Time

coordinates

:

[ 0.26 0.52 ... 86.76 87.02] min

labels

:

...

[ Verknüpftes Spektrum bei 0,260 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

±½@
ö»Îö¿¹·j»D»t4»*
»Ý©S»£°»ýóÉº-êÒº¿p»¢»

»[ýéºùH|»
»Íu»ää©ºLÊÐºsöþºvÐmºÎG4ºCà¦ºÔÍºÕ
º¹¶:»ñ&Rº*
Ò:Ý¿9WÅEºÓo
ºB'ÆºP|_º)ïQ9OÌºÜìº-ºÁrï¹ç
±ºð¹¯ºqHºIKº Verknüpftes Spektrum bei 0,519 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

îÂ@
y¼»¾¯^¹¸:¸4êº=x»ØÃ1»¼-øºÿÉºÓÅº[L»×

»£Êp»Õ@»¤(»ëð¢¹±w 95ºÍ9ÔºÓà³ºOuºÔ
Ü¹ºm¼BºâÞå7ÛÍ9Mº,ºJq:ò49 jZºLÛº¬ëºÃi¹;ý:ì) º)vÂº¡ê¹xÕ½·?³º©ëÁºÀ
ºó(
¹ ...
Verknüpftes Spektrum bei 86,756 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

ã!@
¡Ê r <«><
é;òÙ;\
<â½Ä;²;ÿÐæ;¤ññ;°Í¹;¡´;ÛYÎ;?å;Þ¦ò;²ø§;hAe;°TÉ;ó÷Ì;dK;úºY;ÛýÀ;j8;ïkª;#G;¸Û;Öþ;
Verknüpftes Spektrum bei 87,016 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

0+@
+1Ú<9}í< B <µ*<;µÖ;ÓÆ" <½²ò;ç<àP<ãâ;4Å;vüö;ÑMà;·S;M;HÃà;¾cö;M¶ü;C;Ö[#;3Ó;*Ûæ;6;ýH;R±Ã;ÍN;í;j7;oÚ;ú;]

1: NDDataset [merged [omnic]] — float64, shape: (y:4, x:5549), a.u.

name

:

merged [omnic]

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:00+00:00

description

:

Concatenation of 1 datasets:
( merged [omnic] )

history

:

2026-07-30 19:08:00+00:00> Created by concatenate
2026-07-30 19:08:00+00:00> Merged from several files

Data

title

:

absorbance

values

:

...

[[ 1.544 1.543 ... 2.1 2.091]
[ 1.552 1.553 ... 2.161 2.109]
[ 1.461 1.46 ... 2.087 2.088]
[ 1.448 1.447 ... 2.071 2.065]] a.u.

shape

:

(y:4, x:5549)

Dimension `x`

size

:

5549

title

:

wavenumbers

coordinates

:

[ 6000 5999 ... 650.9 649.9] cm⁻¹

Dimension `y`

size

:

4

title

:

acquisition timestamp (GMT)

coordinates

:

[1.544e+09 1.544e+09 1.544e+09 1.544e+09] s

labels

:

...

[[ 2018-11-30 07:10:57+00:00 2018-11-30 07:22:52+00:00 2018-11-30 07:34:49+00:00 2018-11-30 07:46:44+00:00]
[ /home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_101.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_102.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_103.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_104.SPA]]

The above command reads all files in the DATADIR/irdata/subdir/ directory and merges them into two groups of compatible NDDatasets:

a first NDDataset object (id: 0, shape [335,1868]) comes from the single .srs file.
a second NDDataset object (id: 1, shape [335,1868]) comes from the merging of four .spa files.

Merging compatible NDDatasets is the default behavior of read_dir (or, equivalently, read). If you want to read the files separately, you can use the merge=False keyword:

[9]:

scp.read_dir(folder, merge=False)

[9]:

List (len=5, type=NDDataset)

0: NDDataset [7_CZ0-100 Pd_101] — float64, shape: (y:1, x:5549), a.u.

name

:

7_CZ0-100 Pd_101

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:00+00:00

description

:

# Omnic name: 7_CZ0-100 Pd_101
# Filename: 7_CZ0-100_Pd_101.SPA

history

:

2026-07-30 19:08:00+00:00> Imported from spa file(s)
2026-07-30 19:08:00+00:00> Data processing history from Omnic :
------------------------------------
Acquisition échantillon

<br/> Background acquis le Ven Nov 30 08:03:45 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

<br/> Background acquis le Ven Nov 30 08:12:56 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

<br/> Background acquis le Ven Nov 30 08:24:51 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

<br/> Background acquis le Ven Nov 30 08:36:47 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

As expected the result is a list of 5 NDDataset objects, one for each file in the directory.

Additional options for reading directories

The read_dir/read function has additional options to control the behavior of the reading process:

recursive: if True, the function scans the directory recursively and reads all supported files in all subdirectories.
pattern: a string or a list of strings that can be used to filter the files to be read. Only files whose name matches the pattern will be read.

Let’s see an example with the recursive option:

First we list files in all directories under DATADIR/irdata/subdir/:

[10]:

[str(item.relative_to(DATADIR)) for item in folder.glob("**/*.*")]

[10]:

['irdata/subdir/7_CZ0-100_Pd_102.SPA',
 'irdata/subdir/TGAIR-unreadable.srs',
 'irdata/subdir/7_CZ0-100_Pd_103.SPA',
 'irdata/subdir/7_CZ0-100_Pd_104.SPA',
 'irdata/subdir/7_CZ0-100_Pd_101.SPA',
 'irdata/subdir/1-20/7_CZ0-100_Pd_4.SPA',
 'irdata/subdir/1-20/7_CZ0-100_Pd_3.SPA',
 'irdata/subdir/1-20/7_CZ0-100_Pd_5.SPA',
 'irdata/subdir/20-50/7_CZ0-100_Pd_21.SPA']

The irdata/subdir/ directory contains two subdirectories, 1-20 and 20-50, with additional .spa files.

Now we read all files (a total of 9) in the DATADIR/irdata/subdir/ directory and its subdirectories:

[11]:

scp.read_dir(folder, recursive=True, merge=False)

[11]:

List (len=9, type=NDDataset)

0: NDDataset [7_CZ0-100 Pd_3] — float64, shape: (y:1, x:5549), a.u.

name

:

7_CZ0-100 Pd_3

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:01+00:00

description

:

# Omnic name: 7_CZ0-100 Pd_3
# Filename: 7_CZ0-100_Pd_3.SPA

history

:

2026-07-30 19:08:01+00:00> Imported from spa file(s)
2026-07-30 19:08:01+00:00> Data processing history from Omnic :
------------------------------------
Acquisition échantillon

<br/> Background acquis le Jeu Nov 29 17:13:06 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

<br/> Background acquis le Jeu Nov 29 17:25:03 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

<br/> Background acquis le Jeu Nov 29 17:36:59 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

<br/> Background acquis le Jeu Nov 29 20:48:01 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

<br/> Background acquis le Ven Nov 30 08:03:45 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

<br/> Background acquis le Ven Nov 30 08:12:56 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

<br/> Background acquis le Ven Nov 30 08:24:51 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

<br/> Background acquis le Ven Nov 30 08:36:47 2018 (GMT+01:00) <br/> Format Final : Absorbance <br/> Résolution: 4,000 de 649,9207 à 5999,7134 <br/> Roue de validation: 0 <br/> Roue porte écran atténuation: Vide <br/> Numéro Série du banc:ALK1100494</div></div></div>

and we allow merging them:

[12]:

scp.read_dir(folder, recursive=True)

[12]:

List (len=2, type=NDDataset)

0: NDDataset [dd_6.6_19039_538] — float64, shape: (y:335, x:1868), a.u.

name

:

dd_6.6_19039_538

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:01+00:00

description

:

Concatenation of 1 datasets:
( dd_6.6_19039_538 )

history

:

2026-07-30 19:08:01+00:00> Created by concatenate
2026-07-30 19:08:01+00:00> Merged from several files

Data

title

:

absorbance

values

:

...

[[-0.007524 -0.0003661 ... 8.291e-05 9.239e-05]
[-0.009306 -0.002252 ... 0.0001051 0.000107]
...
[ 0.02474 0.02814 ... 0.002962 0.002967]
[ 0.02663 0.02899 ... 0.002907 0.002916]] a.u.

shape

:

(y:335, x:1868)

Dimension `x`

size

:

1868

title

:

wavenumbers

coordinates

:

[ 4000 3998 ... 401.1 399.2] cm⁻¹

Dimension `y`

size

:

335

title

:

Time

coordinates

:

[ 0.26 0.52 ... 86.76 87.02] min

labels

:

...

[ Verknüpftes Spektrum bei 0,260 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

±½@
ö»Îö¿¹·j»D»t4»*
»Ý©S»£°»ýóÉº-êÒº¿p»¢»

»[ýéºùH|»
»Íu»ää©ºLÊÐºsöþºvÐmºÎG4ºCà¦ºÔÍºÕ
º¹¶:»ñ&Rº*
Ò:Ý¿9WÅEºÓo
ºB'ÆºP|_º)ïQ9OÌºÜìº-ºÁrï¹ç
±ºð¹¯ºqHºIKº Verknüpftes Spektrum bei 0,519 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

îÂ@
y¼»¾¯^¹¸:¸4êº=x»ØÃ1»¼-øºÿÉºÓÅº[L»×

»£Êp»Õ@»¤(»ëð¢¹±w 95ºÍ9ÔºÓà³ºOuºÔ
Ü¹ºm¼BºâÞå7ÛÍ9Mº,ºJq:ò49 jZºLÛº¬ëºÃi¹;ý:ì) º)vÂº¡ê¹xÕ½·?³º©ëÁºÀ
ºó(
¹ ...
Verknüpftes Spektrum bei 86,756 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

ã!@
¡Ê r <«><
é;òÙ;\
<â½Ä;²;ÿÐæ;¤ññ;°Í¹;¡´;ÛYÎ;?å;Þ¦ò;²ø§;hAe;°TÉ;ó÷Ì;dK;úºY;ÛýÀ;j8;ïkª;#G;¸Û;Öþ;
Verknüpftes Spektrum bei 87,016 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

0+@
+1Ú<9}í< B <µ*<;µÖ;ÓÆ" <½²ò;ç<àP<ãâ;4Å;vüö;ÑMà;·S;M;HÃà;¾cö;M¶ü;C;Ö[#;3Ó;*Ûæ;6;ýH;R±Ã;ÍN;í;j7;oÚ;ú;]

1: NDDataset [merged [omnic]] — float64, shape: (y:8, x:5549), a.u.

name

:

merged [omnic]

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:01+00:00

description

:

Concatenation of 1 datasets:
( merged [omnic] )

history

:

2026-07-30 19:08:01+00:00> Created by concatenate
2026-07-30 19:08:01+00:00> Merged from several files

Data

title

:

absorbance

values

:

...

[[ 1.245 1.245 ... 1.311 1.307]
[ 1.245 1.245 ... 1.302 1.299]
...
[ 1.461 1.46 ... 2.087 2.088]
[ 1.448 1.447 ... 2.071 2.065]] a.u.

shape

:

(y:8, x:5549)

Dimension `x`

size

:

5549

title

:

wavenumbers

coordinates

:

[ 6000 5999 ... 650.9 649.9] cm⁻¹

Dimension `y`

size

:

8

title

:

acquisition timestamp (GMT)

coordinates

:

[1.544e+09 1.544e+09 ... 1.544e+09 1.544e+09] s

labels

:

...

[[ 2018-11-29 16:23:05+00:00 2018-11-29 16:35:00+00:00 ... 2018-11-30 07:34:49+00:00 2018-11-30 07:46:44+00:00]
[ /home/runner/.spectrochempy/testdata/irdata/subdir/1-20/7_CZ0-100_Pd_3.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/1-20/7_CZ0-100_Pd_4.SPA ...
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_103.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_104.SPA]]

As the 8 .spa files are compatible, they are merged into a single NDDataset object. The .srs file is read separately.

A specific reader can equivalently read the folder recursively:

[13]:

scp.read_omnic(folder, recursive=True)

[13]:

List (len=2, type=NDDataset)

0: NDDataset [dd_6.6_19039_538] — float64, shape: (y:335, x:1868), a.u.

name

:

dd_6.6_19039_538

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:01+00:00

description

:

Concatenation of 1 datasets:
( dd_6.6_19039_538 )

history

:

2026-07-30 19:08:01+00:00> Created by concatenate
2026-07-30 19:08:01+00:00> Merged from several files

Data

title

:

absorbance

values

:

...

[[-0.007524 -0.0003661 ... 8.291e-05 9.239e-05]
[-0.009306 -0.002252 ... 0.0001051 0.000107]
...
[ 0.02474 0.02814 ... 0.002962 0.002967]
[ 0.02663 0.02899 ... 0.002907 0.002916]] a.u.

shape

:

(y:335, x:1868)

Dimension `x`

size

:

1868

title

:

wavenumbers

coordinates

:

[ 4000 3998 ... 401.1 399.2] cm⁻¹

Dimension `y`

size

:

335

title

:

Time

coordinates

:

[ 0.26 0.52 ... 86.76 87.02] min

labels

:

...

[ Verknüpftes Spektrum bei 0,260 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

±½@
ö»Îö¿¹·j»D»t4»*
»Ý©S»£°»ýóÉº-êÒº¿p»¢»

»[ýéºùH|»
»Íu»ää©ºLÊÐºsöþºvÐmºÎG4ºCà¦ºÔÍºÕ
º¹¶:»ñ&Rº*
Ò:Ý¿9WÅEºÓo
ºB'ÆºP|_º)ïQ9OÌºÜìº-ºÁrï¹ç
±ºð¹¯ºqHºIKº Verknüpftes Spektrum bei 0,519 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

îÂ@
y¼»¾¯^¹¸:¸4êº=x»ØÃ1»¼-øºÿÉºÓÅº[L»×

»£Êp»Õ@»¤(»ëð¢¹±w 95ºÍ9ÔºÓà³ºOuºÔ
Ü¹ºm¼BºâÞå7ÛÍ9Mº,ºJq:ò49 jZºLÛº¬ëºÃi¹;ý:ì) º)vÂº¡ê¹xÕ½·?³º©ëÁºÀ
ºó(
¹ ...
Verknüpftes Spektrum bei 86,756 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

ã!@
¡Ê r <«><
é;òÙ;\
<â½Ä;²;ÿÐæ;¤ññ;°Í¹;¡´;ÛYÎ;?å;Þ¦ò;²ø§;hAe;°TÉ;ó÷Ì;dK;úºY;ÛýÀ;j8;ïkª;#G;¸Û;Öþ;
Verknüpftes Spektrum bei 87,016 Min.
Ë
ÁØsúË
t/ä
/ä
/ä

0+@
+1Ú<9}í< B <µ*<;µÖ;ÓÆ" <½²ò;ç<àP<ãâ;4Å;vüö;ÑMà;·S;M;HÃà;¾cö;M¶ü;C;Ö[#;3Ó;*Ûæ;6;ýH;R±Ã;ÍN;í;j7;oÚ;ú;]

1: NDDataset [merged [omnic]] — float64, shape: (y:8, x:5549), a.u.

name

:

merged [omnic]

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:01+00:00

description

:

Concatenation of 1 datasets:
( merged [omnic] )

history

:

2026-07-30 19:08:01+00:00> Created by concatenate
2026-07-30 19:08:01+00:00> Merged from several files

Data

title

:

absorbance

values

:

...

[[ 1.245 1.245 ... 1.311 1.307]
[ 1.245 1.245 ... 1.302 1.299]
...
[ 1.461 1.46 ... 2.087 2.088]
[ 1.448 1.447 ... 2.071 2.065]] a.u.

shape

:

(y:8, x:5549)

Dimension `x`

size

:

5549

title

:

wavenumbers

coordinates

:

[ 6000 5999 ... 650.9 649.9] cm⁻¹

Dimension `y`

size

:

8

title

:

acquisition timestamp (GMT)

coordinates

:

[1.544e+09 1.544e+09 ... 1.544e+09 1.544e+09] s

labels

:

...

[[ 2018-11-29 16:23:05+00:00 2018-11-29 16:35:00+00:00 ... 2018-11-30 07:34:49+00:00 2018-11-30 07:46:44+00:00]
[ /home/runner/.spectrochempy/testdata/irdata/subdir/1-20/7_CZ0-100_Pd_3.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/1-20/7_CZ0-100_Pd_4.SPA ...
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_103.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_104.SPA]]

Let’s see an example with the pattern option:

We read all files in the DATADIR/irdata/subdir/ directory and its subdirectories, but only those with the .spa extension and whose name contains the string 4:

[14]:

scp.read_dir(folder, recursive=True, pattern="*4*")

[14]:

NDDataset [merged [omnic]] — float64, shape: (y:2, x:5549), a.u.

name

:

merged [omnic]

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:02+00:00

description

:

Concatenation of 2 datasets:
( 7_CZ0-100 Pd_4, 7_CZ0-100 Pd_104 )

history

:

2026-07-30 19:08:02+00:00> Created by concatenate
2026-07-30 19:08:02+00:00> Merged from several files

Data

title

:

absorbance

values

:

[[ 1.245 1.245 ... 1.302 1.299]
[ 1.448 1.447 ... 2.071 2.065]] a.u.

shape

:

(y:2, x:5549)

Dimension `x`

size

:

5549

title

:

wavenumbers

coordinates

:

[ 6000 5999 ... 650.9 649.9] cm⁻¹

Dimension `y`

size

:

2

title

:

acquisition timestamp (GMT)

coordinates

:

[1.544e+09 1.544e+09] s

labels

:

[[ 2018-11-29 16:35:00+00:00 2018-11-30 07:46:44+00:00]
[ /home/runner/.spectrochempy/testdata/irdata/subdir/1-20/7_CZ0-100_Pd_4.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_104.SPA]]

The above pattern *4* matches only two files, which are then merged and returned as a single NDDataset object.

This pattern option is obviously interesting to select only a type of extension:

[15]:

scp.read_dir(folder, recursive=True, pattern="*.spa")

[15]:

NDDataset [merged [omnic]] — float64, shape: (y:8, x:5549), a.u.

name

:

merged [omnic]

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:02+00:00

description

:

Concatenation of 8 datasets:
( 7_CZ0-100 Pd_3, 7_CZ0-100 Pd_4, 7_CZ0-100 Pd_5, 7_CZ0-100 Pd_21, 7_CZ0-100 Pd_101, 7_CZ0-100 Pd_102, 7_CZ0-100 Pd_103, 7_CZ0-100 Pd_104 )

history

:

2026-07-30 19:08:02+00:00> Created by concatenate
2026-07-30 19:08:02+00:00> Merged from several files

Data

title

:

absorbance

values

:

[[ 1.245 1.245 ... 1.311 1.307]
[ 1.245 1.245 ... 1.302 1.299]
...
[ 1.461 1.46 ... 2.087 2.088]
[ 1.448 1.447 ... 2.071 2.065]] a.u.

shape

:

(y:8, x:5549)

Dimension `x`

size

:

5549

title

:

wavenumbers

coordinates

:

[ 6000 5999 ... 650.9 649.9] cm⁻¹

Dimension `y`

size

:

8

title

:

acquisition timestamp (GMT)

coordinates

:

[1.544e+09 1.544e+09 ... 1.544e+09 1.544e+09] s

labels

:

[[ 2018-11-29 16:23:05+00:00 2018-11-29 16:35:00+00:00 ... 2018-11-30 07:34:49+00:00 2018-11-30 07:46:44+00:00]
[ /home/runner/.spectrochempy/testdata/irdata/subdir/1-20/7_CZ0-100_Pd_3.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/1-20/7_CZ0-100_Pd_4.SPA ...
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_103.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_104.SPA]]

[16]:

scp.read(folder, recursive=True, pattern="*.spa")  # equivalent

[16]:

NDDataset [merged [omnic]] — float64, shape: (y:8, x:5549), a.u.

name

:

merged [omnic]

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:02+00:00

description

:

Concatenation of 8 datasets:
( 7_CZ0-100 Pd_3, 7_CZ0-100 Pd_4, 7_CZ0-100 Pd_5, 7_CZ0-100 Pd_21, 7_CZ0-100 Pd_101, 7_CZ0-100 Pd_102, 7_CZ0-100 Pd_103, 7_CZ0-100 Pd_104 )

history

:

2026-07-30 19:08:02+00:00> Created by concatenate
2026-07-30 19:08:02+00:00> Merged from several files

Data

title

:

absorbance

values

:

[[ 1.245 1.245 ... 1.311 1.307]
[ 1.245 1.245 ... 1.302 1.299]
...
[ 1.461 1.46 ... 2.087 2.088]
[ 1.448 1.447 ... 2.071 2.065]] a.u.

shape

:

(y:8, x:5549)

Dimension `x`

size

:

5549

title

:

wavenumbers

coordinates

:

[ 6000 5999 ... 650.9 649.9] cm⁻¹

Dimension `y`

size

:

8

title

:

acquisition timestamp (GMT)

coordinates

:

[1.544e+09 1.544e+09 ... 1.544e+09 1.544e+09] s

labels

:

[[ 2018-11-29 16:23:05+00:00 2018-11-29 16:35:00+00:00 ... 2018-11-30 07:34:49+00:00 2018-11-30 07:46:44+00:00]
[ /home/runner/.spectrochempy/testdata/irdata/subdir/1-20/7_CZ0-100_Pd_3.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/1-20/7_CZ0-100_Pd_4.SPA ...
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_103.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_104.SPA]]

[17]:

scp.read_omnic(folder, recursive=True, pattern="*4.spa")  # equivalent

[17]:

NDDataset [merged [omnic]] — float64, shape: (y:2, x:5549), a.u.

name

:

merged [omnic]

author

:

runner@runnervmvrwv9

created

:

2026-07-30 19:08:02+00:00

description

:

Concatenation of 2 datasets:
( 7_CZ0-100 Pd_4, 7_CZ0-100 Pd_104 )

history

:

2026-07-30 19:08:02+00:00> Created by concatenate
2026-07-30 19:08:02+00:00> Merged from several files

Data

title

:

absorbance

values

:

[[ 1.245 1.245 ... 1.302 1.299]
[ 1.448 1.447 ... 2.071 2.065]] a.u.

shape

:

(y:2, x:5549)

Dimension `x`

size

:

5549

title

:

wavenumbers

coordinates

:

[ 6000 5999 ... 650.9 649.9] cm⁻¹

Dimension `y`

size

:

2

title

:

acquisition timestamp (GMT)

coordinates

:

[1.544e+09 1.544e+09] s

labels

:

[[ 2018-11-29 16:35:00+00:00 2018-11-30 07:46:44+00:00]
[ /home/runner/.spectrochempy/testdata/irdata/subdir/1-20/7_CZ0-100_Pd_4.SPA
/home/runner/.spectrochempy/testdata/irdata/subdir/7_CZ0-100_Pd_104.SPA]]

This way the “.srs” file is ignored.

Reading files from a ZIP archive

The read_zip function is designed to read files from a ZIP archive. It can be used to read a single file or all files in the archive. As usual, by default all files are merged. The merge keyword can be used to read the files separately.

[18]:

scp.read(
    "https://eigenvector.com/wp-content/uploads/2019/06/corn.mat_.zip", merge=False
)

INFO | The mat file contains an array of strings named 'information' which will not be converted to NDDataset

[18]:

List (len=7, type=NDDataset)

0: NDDataset [m5nbs] — float64, shape: (y:3, x:700)

name

:

m5nbs

author

:

*unknown*

created

:

2026-07-30 19:08:02+00:00

history

:

2026-07-30 19:08:02+00:00> M5nbs.axisscale{2,1}=freqs; %class double 1x700
2026-07-30 19:08:02+00:00> Imported by spectrochempy.

Data

title

:

values

:

...

[[ 0.134 0.132 ... 0.09665 0.09771]
[ 0.1374 0.1354 ... 0.09898 0.09999]
[ 0.1437 0.1416 ... 0.1037 0.1048]]

shape

:

(y:3, x:700)

Dimension `x`

size

:

700

title

:

coordinates

:

[ 1100 1102 ... 2496 2498]

Dimension `y`

size

:

3

title

:

index

coordinates

:

[ 0 1 2]

1: NDDataset [mp5nbs] — float64, shape: (y:4, x:700)

name

:

mp5nbs

author

:

*unknown*

created

:

2026-07-30 19:08:02+00:00

history

:

2026-07-30 19:08:02+00:00> Mp5nbs.axisscale{2,1}=freqs; %class double 1x700
2026-07-30 19:08:02+00:00> Imported by spectrochempy.

Data

title

:

values

:

...

[[ 0.06679 0.0645 ... 0.03636 0.03724]
[ 0.06031 0.05819 ... 0.02807 0.02892]
[ 0.06189 0.05965 ... 0.03215 0.03299]
[ 0.06115 0.05901 ... 0.03013 0.03099]]

shape

:

(y:4, x:700)

Dimension `x`

size

:

700

title

:

coordinates

:

[ 1100 1102 ... 2496 2498]

Dimension `y`

size

:

4

title

:

index

coordinates

:

[ 0 1 2 3]

2: NDDataset [mp6nbs] — float64, shape: (y:4, x:700)

name

:

mp6nbs

author

:

*unknown*

created

:

2026-07-30 19:08:02+00:00

history

:

2026-07-30 19:08:02+00:00> Mp6nbs.axisscale{2,1}=freqs; %class double 1x700
2026-07-30 19:08:02+00:00> Imported by spectrochempy.

Data

title

:

values

:

...

[[ 0.05404 0.0524 ... 0.02147 0.02236]
[ 0.05388 0.05223 ... 0.02187 0.02277]
[ 0.05238 0.05069 ... 0.02207 0.02295]
[ 0.05311 0.05144 ... 0.02195 0.02284]]

shape

:

(y:4, x:700)

Dimension `x`

size

:

700

title

:

coordinates

:

[ 1100 1102 ... 2496 2498]

Dimension `y`

size

:

4

title

:

index

coordinates

:

[ 0 1 2 3]

3: NDDataset [propvals] — float64, shape: (y:80, x:4)

name

:

propvals

author

:

*unknown*

created

:

2026-07-30 19:08:02+00:00

history

:

2026-07-30 19:08:02+00:00> Propvals.label{2,1}=proplbls; %class char 4x8
2026-07-30 19:08:02+00:00> Imported by spectrochempy.

Data

title

:

values

:

...

[[ 10.45 3.687 8.746 64.84]
[ 10.41 3.72 8.658 64.85]
...
[ 10.59 3.176 8.132 65.21]
[ 10.98 3.328 8.428 64.85]]

shape

:

(y:80, x:4)

Dimension `x`

size

:

4

title

:

labels

:

[ Moisture Oil Protein Starch ]

Dimension `y`

size

:

80

title

:

index

coordinates

:

[ 0 1 ... 78 79]

4: NDDataset [m5spec] — float64, shape: (y:80, x:700)

name

:

m5spec

author

:

*unknown*

created

:

2026-07-30 19:08:02+00:00

history

:

2026-07-30 19:08:02+00:00> M5spec.axisscale{2,1}=freqs; %class double 1x700
2026-07-30 19:08:02+00:00> Imported by spectrochempy.

Data

title

:

values

:

...

[[ 0.04449 0.04438 ... 0.7309 0.7306]
[ 0.0465 0.04635 ... 0.7275 0.727]
...
[ 0.05944 0.05932 ... 0.7362 0.7357]
[ 0.05009 0.04993 ... 0.7289 0.7282]]

shape

:

(y:80, x:700)

Dimension `x`

size

:

700

title

:

coordinates

:

[ 1100 1102 ... 2496 2498]

Dimension `y`

size

:

80

title

:

index

coordinates

:

[ 0 1 ... 78 79]

5: NDDataset [mp5spec] — float64, shape: (y:80, x:700)

name

:

mp5spec

author

:

*unknown*

created

:

2026-07-30 19:08:02+00:00

history

:

2026-07-30 19:08:02+00:00> Mp5spec.axisscale{2,1}=freqs; %class double 1x700
2026-07-30 19:08:02+00:00> Imported by spectrochempy.

Data

title

:

values

:

...

[[-0.01244 -0.01251 ... 0.6849 0.6844]
[-0.01419 -0.01425 ... 0.6924 0.6919]
...
[0.005116 0.00504 ... 0.6979 0.6976]
[-0.003701 -0.003818 ... 0.7128 0.7121]]

shape

:

(y:80, x:700)

Dimension `x`

size

:

700

title

:

coordinates

:

[ 1100 1102 ... 2496 2498]

Dimension `y`

size

:

80

title

:

index

coordinates

:

[ 0 1 ... 78 79]

6: NDDataset [mp6spec] — float64, shape: (y:80, x:700)

name

:

mp6spec

author

:

*unknown*

created

:

2026-07-30 19:08:02+00:00

history

:

2026-07-30 19:08:02+00:00> Mp6spec.axisscale{2,1}=freqs; %class double 1x700
2026-07-30 19:08:02+00:00> Imported by spectrochempy.

Data

title

:

values

:

...

[[ -0.0227 -0.0228 ... 0.6747 0.6741]
[-0.02192 -0.02206 ... 0.6826 0.6822]
...
[-0.006796 -0.006888 ... 0.6899 0.6891]
[-0.01526 -0.01538 ... 0.7028 0.7021]]

shape

:

(y:80, x:700)

Dimension `x`

size

:

700

title

:

coordinates

:

[ 1100 1102 ... 2496 2498]

Dimension `y`

size

:

80

title

:

index

coordinates

:

[ 0 1 ... 78 79]