Warning

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

Note

Go to the end to download the full example code

2D-IRIS analysis example

In this example, we perform the 2D IRIS analysis of CO adsorption on a sulfide catalyst.

import spectrochempy as scp

Uploading dataset

X = scp.read("irdata/CO@Mo_Al2O3.SPG")

X has two coordinates: * wavenumbers named “x” * and timestamps (i.e., the time of recording) named “y”.

print(X.coordset)

CoordSet: [x:wavenumbers, y:acquisition timestamp (GMT)]

Setting new coordinates

The y coordinates of the dataset is the acquisition timestamp. However, each spectrum has been recorded with a given pressure of CO in the infrared cell.

Hence, it would be interesting to add pressure coordinates to the y dimension:

pressures = [
    0.003,
    0.004,
    0.009,
    0.014,
    0.021,
    0.026,
    0.036,
    0.051,
    0.093,
    0.150,
    0.203,
    0.300,
    0.404,
    0.503,
    0.602,
    0.702,
    0.801,
    0.905,
    1.004,
]

c_pressures = scp.Coord(pressures, title="pressure", units="torr")

Now we can set multiple coordinates:

c_times = X.y.copy()  # the original coordinate
X.y = [c_times, c_pressures]
print(X.y)

CoordSet: [_1:acquisition timestamp (GMT), _2:pressure]

To get a detailed a rich display of these coordinates. In a jupyter notebook, just type:

X.coordset
DIMENSION `x`
size 3112
title wavenumbers
coordinates
[ 4000 3999 ... 1001 999.9] cm⁻¹
DIMENSION `y`
size 19
(_1)
title acquisition timestamp (GMT)
coordinates
[1.477e+09 1.477e+09 ... 1.477e+09 1.477e+09] s
labels
[[ 2016-10-18 13:49:35+00:00 2016-10-18 13:54:06+00:00 ... 2016-10-18 16:01:33+00:00 2016-10-18 16:06:37+00:00]
[ *Résultat de Soustraction:04_Mo_Al2O3_calc_0.003torr_LT_after sulf_Oct 18 15:46:42 2016 (GMT+02:00)
*Résultat de Soustraction:04_Mo_Al2O3_calc_0.004torr_LT_after sulf_Oct 18 15:51:12 2016 (GMT+02:00) ...
*Résultat de Soustraction:04_Mo_Al2O3_calc_0.905torr_LT_after sulf_Oct 18 17:58:42 2016 (GMT+02:00)
*Résultat de Soustraction:04_Mo_Al2O3_calc_1.004torr_LT_after sulf_Oct 18 18:03:41 2016 (GMT+02:00)]]
(_2)
title pressure
coordinates
[ 0.003 0.004 ... 0.905 1.004] torr


By default, the current coordinate is the first one (here c_times ). For example, it will be used by default for plotting:

prefs = X.preferences
prefs.figure.figsize = (7, 3)
_ = X.plot(colorbar=True)
_ = X.plot_map(colorbar=True)
  • plot iris
  • plot iris

To seamlessly work with the second coordinates (pressures), we can change the default coordinate:

X.y.select(2)  # to select coordinate `_2`
X.y.default
size 19
title pressure
coordinates
[ 0.003 0.004 ... 0.905 1.004] torr


Let’s now plot the spectral range of interest. The default coordinate is now used:

X_ = X[:, 2250.0:1950.0]
print(X_.y.default)
_ = X_.plot()
_ = X_.plot_map()
  • plot iris
  • plot iris
Coord: [float64] torr (size: 19)

IRIS analysis without regularization

Perform IRIS without regularization (the loglevel can be set to INFO to have information on the running process)

iris1 = scp.IRIS(log_level="INFO")

first we compute the kernel object

K = scp.IrisKernel(X_, "langmuir", q=[-8, -1, 50])

Creating Kernel...
Kernel now ready as IrisKernel().kernel!

The actual kernel is given by the kernel attribute

kernel = K.kernel
kernel
name langmuir kernel matrix
author runner@fv-az1106-694
created 2024-04-27 03:03:02+02:00
DATA
title coverage
values
[[ 0.06424 0.1265 ... 0.001332 0.0005778]
[ 0.0659 0.1303 ... 0.00177 0.0007683]
...
[ 0.0714 0.1428 ... 0.1056 0.05078]
[ 0.0714 0.1428 ... 0.1084 0.05227]]
shape (y:19, x:50)
DIMENSION `x`
size 50
title $\Delta_{ads}G^{0}/RT$
coordinates
[ -8 -7.857 ... -1.143 -1]
DIMENSION `y`
size 19
title pressure
coordinates
[ 0.003 0.004 ... 0.905 1.004] torr


Now we fit the model - we can pass either the Kernel object or the kernel NDDataset

iris1.fit(X_, K)

 Build S matrix (sharpness)
 ... done
 Solving for 312 channels and 19 observations, no regularization
 -->  residuals = 1.09e-01    curvature = 9.14e+04
 Done.

<spectrochempy.analysis.decomposition.iris.IRIS object at 0x7f1a3603a980>

Plots the results

iris1.plotdistribution()
_ = iris1.plotmerit()
  • 2D IRIS distribution, $\lambda$ = 0.00e+00
  • 2D IRIS merit plot, $\lambda$ = 0.00e+00