Using IBIS8542Model¶

This is an example showing how to fit an array of spectra using the mcalf.models.IBIS8542Model class, and plot and export the results.

Generate sample data¶

Create spectral grid¶

First, we shall generate some random sample data to demonstrate the API. The randomly generated spectra are not intended to be representative of observed spectra numerically, they just have a similar shape.

import numpy as np
import matplotlib.pyplot as plt

from mcalf.profiles.voigt import voigt, double_voigt
from mcalf.visualisation import plot_spectrum

# Create demo wavelength grid
w = np.linspace(8541, 8543, 20)


We shall generate demo spectra in a 2x3 grid. Half of the spectra will have one spectral component (absorption only) and the other half will have two spectral components (mixed absorption and emission components).

Demo spectral components will be modelled as Voigt functions with randomly generated parameters, each within a set range of values.

def v(classification, w, *args):
"""Voigt function wrapper."""
if classification < 1.5:  # absorption only
return voigt(w, *args[:4], args[-1])
return double_voigt(w, *args)  # absorption + emission

def s():
"""Generate random spectra for a 2x3 grid."""
np.random.seed(0)  # same spectra every time
p = np.random.rand(9, 6)  # random Voigt parameters
# 0  1  2  3  4  5  6  7  8  # p index
# a1 b1 s1 g1 a2 b2 s2 g2 d  # Voigt parameter
# absorption |emission   |background

p[0] = 100 * p[0] - 1000  # absorption amplitude
p[4] = 100 * p[4] + 1000  # emission amplitude

for i in (1, 5):  # abs. and emi. peak positions
p[i] = 0.05 * p[i] - 0.025 + 8542

for i in (2, 3, 6, 7):  # Voigt sigmas and gammas
p[i] = 0.1 * p[i] + 0.1

p[8] = 300 * p[8] + 2000  # intensity background constant

# Define each spectrum's classification
c = [0, 2, 0, 2, 0, 2]
# Choose single or double component spectrum
# based on this inside the function v().

# Generate the spectra
specs = [v(c[i], w, *p[:, i]) for i in range(6)]

# Reshape to 2x3 grid
return np.asarray(specs).reshape((2, 3, len(w)))

raw_data = s()

print('shape of spectral grid:', raw_data.shape)


Out:

shape of spectral grid: (2, 3, 20)


These spectra look as follows,

fig, axes = plt.subplots(2, 3, constrained_layout=True)
for ax, spec in zip(axes.flat, raw_data.reshape((6, raw_data.shape[-1]))):

plot_spectrum(w, spec, normalised=False, ax=ax)

plt.show()


Compute the background intensity¶

MCALF does not model a constant background value, i.e., the fitting process assumes the intensity values far out in the spectrum’s wings are zero.

You can however tell MCALF what the constant background value is, and it will subtract it from the spectrum before fitting.

This process was not made automatic as we wanted to give the user full control on setting the background value.

For these demo data, we shall simply set the background to the first intensity value of every spectrum. For a real dataset, you may wish to average the background value throughout a range of spectral points, or even do a moving average throughout time. Functions are provided in mcalf.utils.smooth to assist with this process.

backgrounds = raw_data[:, :, 0]

print('shape of background intensity grid:', backgrounds.shape)


Out:

shape of background intensity grid: (2, 3)


Define a demo classifier¶

In this example we will not demonstrate how to create a neural network classifier. MCALF offers a lot of flexibility when it comes to the classifier. This demo classifier outlines the basic API that is required. By default, the model has a sklearn.neural_network.MLPClassifier object preloaded for use as the classifier. The methods mcalf.models.ModelBase.train() and mcalf.models.ModelBase.test() are provided by MCALF to assist with training the neural network. There is also a useful script in the Getting Started section under User Documentation in the sidebar for semi-automating the process of creating the ground truth dataset.

Please see tutorials for packages such as scikit-learn for more in-depth advice on creating classifiers.

As we only have six spectra with two distinct shapes, we can create a very simple classifier that classifies spectra based on whether their central intensity is greater or smaller than the left most intensity.

class DemoClassifier:
@staticmethod
def predict(X):
y = np.zeros(len(X), dtype=int)
y[X[:, len(X[0]) // 2] > X[:, 0]] = 2
return y


Using IBIS8542Model with the generated data¶

Initialise the model¶

Everything we have been doing up to this point has been creating the demo data and classifier. Now we can actually create a model and load in the demo data, although the following steps would be identical for a real dataset.

from mcalf.models import IBIS8542Model

# Initialise the model with the wavelength grid
model = IBIS8542Model(original_wavelengths=w)

# Load the spectral shape classifier
model.neural_network = DemoClassifier

# Load the array of spectra and background intensities


We have now fully initialised the model. We can now call methods to fit the model to the loaded spectra. In the following step we fit all the loaded spectra and a 1D list of FitResult objects is returned.

fits = model.fit(row=[0, 1], column=range(3))

print(fits)


Out:

Processing 6 spectra
[Successful FitResult with absorption profile of classification 0, Successful FitResult with both profile of classification 2, Successful FitResult with absorption profile of classification 0, Successful FitResult with both profile of classification 2, Successful FitResult with absorption profile of classification 0, Successful FitResult with both profile of classification 2]


Plot the fitted parameters¶

The individual components of each fit can now be plotted separately,

fig, axes = plt.subplots(2, 3, constrained_layout=True)
for ax, fit in zip(axes.flat, fits):

model.plot_separate(fit, show_legend=False, ax=ax)

plt.show()


Extract processed spectra¶

As well as fitting spectra, we can call other methods. In this step we’ll extract the array of loaded spectra. However, these spectra have been re-interpolated to a new finer wavelength grid. (This grid can be customised when initialising the model.)

spectra = model.get_spectra(row=[0, 1], column=range(3))

print('new shape of spectral grid:', spectra.shape)

spectra_1d = spectra.reshape((6, spectra.shape[-1]))

fig, axes = plt.subplots(2, 3, constrained_layout=True)
for ax, spec in zip(axes.flat, spectra_1d):

plot_spectrum(model.constant_wavelengths, spec,
normalised=False, ax=ax)

plt.show()


Out:

new shape of spectral grid: (1, 2, 3, 41)


Classify spectra¶

We can also classify the loaded spectra and create plots,

classifications = model.classify_spectra(row=[0, 1], column=range(3))

print('classifications are:', classifications)


Out:

classifications are: [[[0 2 0]
[2 0 2]]]


This function plots the spectra grouped by classification,

from mcalf.visualisation import plot_classifications

plot_classifications(spectra_1d, classifications.flatten())


Out:

GridSpec(1, 2)


This function plots a spatial map of the classifications on the 2x3 grid,

from mcalf.visualisation import plot_class_map

plot_class_map(classifications)


Out:

<matplotlib.image.AxesImage object at 0x7ff03f88dbb0>


Merge output data¶

The FitResult objects in the fits 1D list can be merged into a grid. Each of these objects can be appended to a single FitResults object. This allows for increased data portability,

from mcalf.models import FitResults

# Initialise with the grid shape and num. params.
results = FitResults((2, 3), 8)

# Append each fit to the object
for fit in fits:
results.append(fit)


Now that we have appended all 6 fits, we can access the merged data,

print(results.classifications)
print(results.profile)
print(results.success)
print(results.parameters)


Out:

[[0 2 0]
[2 0 2]]
[['absorption' 'both' 'absorption']
['both' 'absorption' 'both']]
[[ True  True  True]
[ True  True  True]]
[[[-8.12854070e+02  8.54199697e+03  1.78499714e-01  1.26560703e-01
nan             nan             nan             nan]
[-1.29176198e+03  8.54204025e+03  1.73163470e-01  2.91399148e-02
1.47807034e+03  8.54203294e+03  1.80160852e-01  1.37541715e-04]
[-8.18417945e+02  8.54202321e+03  1.28952238e-01  1.56493579e-01
nan             nan             nan             nan]]

[[-2.21460127e+03  8.54199483e+03  1.06845317e-01  2.21737352e-01
2.33682459e+03  8.54199968e+03  1.48607677e-01  2.05125322e-01]
[-8.73401644e+02  8.54201460e+03  1.15790594e-01  1.20586703e-01
nan             nan             nan             nan]
[-1.32558710e+03  8.54202755e+03  1.38990373e-01  2.37830827e-02
1.48103088e+03  8.54202440e+03  1.49028385e-01  1.97020859e-06]]]


Calculate velocities¶

And finally, we can calculate Doppler velocities for both the quiescent (absorption) and active (emission) regimes. (The model needs to be given so the stationary line core wavelength is available.)

quiescent = results.velocities(model, vtype='quiescent')
active = results.velocities(model, vtype='active')

from mcalf.visualisation import plot_map

fig, axes = plt.subplots(2, constrained_layout=True)
plot_map(quiescent, ax=axes[0])
plot_map(active, ax=axes[1])
plt.show()


Export to FITS file¶

The FitResults object can also be exported to a FITS file,

results.save('ibis8542model_demo_fit.fits', model)


The file has the following structure,

from astropy.io import fits

with fits.open('ibis8542model_demo_fit.fits') as hdul:
hdul.info()


Out:

Filename: ibis8542model_demo_fit.fits
No.    Name      Ver    Type      Cards   Dimensions   Format
0  PRIMARY       1 PrimaryHDU      11   (0,)
1  PARAMETERS    1 ImageHDU        12   (8, 3, 2)   float64
2  CLASSIFICATIONS    1 ImageHDU        10   (3, 2)   int16
3  PROFILE       1 ImageHDU        11   (3, 2)   int16
4  SUCCESS       1 ImageHDU        10   (3, 2)   int16
5  CHI2          1 ImageHDU        10   (3, 2)   float64
6  VLOSA         1 ImageHDU        12   (3, 2)   float64
7  VLOSQ         1 ImageHDU        12   (3, 2)   float64


This example outlines the basics of using the mcalf.models.IBIS8542Model class. Typically millions of spectra would need to be fitted and processed at the same time. For more details on running big jobs and how spectra can be fitted in parallel, see the Working with IBIS data example in the gallery.

Note

Due to limitations with the documentation hosting provider, the examples in the MCALF documentation are computed using a Python based implementation of the Voigt profile instead of the more efficient C version. When this code is run on your own machine it should take much less time than the value reported below.

Total running time of the script: ( 1 minutes 49.796 seconds)

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