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Models#
This is an introduction and overview on how to work with models in Gammapy.
The sub-package gammapy.modeling contains all the functionality related to modeling and fitting data. This includes spectral, spatial and temporal model classes, as well as the fit and parameter API. We will cover the following topics in order:
The models follow a naming scheme which contains the category as a suffix to the class name. An overview of all the available models can be found in the model gallery.
Note that there are separate tutorials, model_management and fitting that explains about gammapy.modeling, the Gammapy modeling and fitting framework. You have to read that to learn how to work with models in order to analyse data.
Setup#
[1]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
[2]:
from astropy import units as u
from gammapy.maps import Map, WcsGeom, MapAxis
Spectral models#
All models are imported from the gammapy.modeling.models namespace. Let’s start with a PowerLawSpectralModel:
[3]:
from gammapy.modeling.models import PowerLawSpectralModel
[4]:
pwl = PowerLawSpectralModel()
print(pwl)
PowerLawSpectralModel
type name value unit error min max frozen is_norm link
-------- --------- ---------- -------------- --------- --- --- ------ ------- ----
spectral index 2.0000e+00 0.000e+00 nan nan False False
spectral amplitude 1.0000e-12 cm-2 s-1 TeV-1 0.000e+00 nan nan False True
spectral reference 1.0000e+00 TeV 0.000e+00 nan nan True False
To get a list of all available spectral models you can import and print the spectral model registry or take a look at the model gallery:
[5]:
from gammapy.modeling.models import SPECTRAL_MODEL_REGISTRY
print(SPECTRAL_MODEL_REGISTRY)
Registry
--------
ConstantSpectralModel : ['ConstantSpectralModel', 'const']
CompoundSpectralModel : ['CompoundSpectralModel', 'compound']
PowerLawSpectralModel : ['PowerLawSpectralModel', 'pl']
PowerLaw2SpectralModel : ['PowerLaw2SpectralModel', 'pl-2']
BrokenPowerLawSpectralModel : ['BrokenPowerLawSpectralModel', 'bpl']
SmoothBrokenPowerLawSpectralModel : ['SmoothBrokenPowerLawSpectralModel', 'sbpl']
PiecewiseNormSpectralModel : ['PiecewiseNormSpectralModel', 'piecewise-norm']
ExpCutoffPowerLawSpectralModel : ['ExpCutoffPowerLawSpectralModel', 'ecpl']
ExpCutoffPowerLaw3FGLSpectralModel : ['ExpCutoffPowerLaw3FGLSpectralModel', 'ecpl-3fgl']
SuperExpCutoffPowerLaw3FGLSpectralModel : ['SuperExpCutoffPowerLaw3FGLSpectralModel', 'secpl-3fgl']
SuperExpCutoffPowerLaw4FGLDR3SpectralModel: ['SuperExpCutoffPowerLaw4FGLDR3SpectralModel', 'secpl-4fgl-dr3']
SuperExpCutoffPowerLaw4FGLSpectralModel : ['SuperExpCutoffPowerLaw4FGLSpectralModel', 'secpl-4fgl']
LogParabolaSpectralModel : ['LogParabolaSpectralModel', 'lp']
TemplateSpectralModel : ['TemplateSpectralModel', 'template']
TemplateNDSpectralModel : ['TemplateNDSpectralModel', 'templateND']
GaussianSpectralModel : ['GaussianSpectralModel', 'gauss']
EBLAbsorptionNormSpectralModel : ['EBLAbsorptionNormSpectralModel', 'ebl-norm']
NaimaSpectralModel : ['NaimaSpectralModel', 'naima']
ScaleSpectralModel : ['ScaleSpectralModel', 'scale']
PowerLawNormSpectralModel : ['PowerLawNormSpectralModel', 'pl-norm']
LogParabolaNormSpectralModel : ['LogParabolaNormSpectralModel', 'lp-norm']
ExpCutoffPowerLawNormSpectralModel : ['ExpCutoffPowerLawNormSpectralModel', 'ecpl-norm']
Spectral models all come with default parameters. Different parameter values can be passed on creation of the model, either as a string defining the value and unit or as an astropy.units.Quantity object directly:
[6]:
amplitude = 1e-12 * u.Unit("TeV-1 cm-2 s-1")
pwl = PowerLawSpectralModel(amplitude=amplitude, index=2.2)
For convenience a str specifying the value and unit can be passed as well:
[7]:
pwl = PowerLawSpectralModel(amplitude="2.7e-12 TeV-1 cm-2 s-1", index=2.2)
print(pwl)
PowerLawSpectralModel
type name value unit error min max frozen is_norm link
-------- --------- ---------- -------------- --------- --- --- ------ ------- ----
spectral index 2.2000e+00 0.000e+00 nan nan False False
spectral amplitude 2.7000e-12 cm-2 s-1 TeV-1 0.000e+00 nan nan False True
spectral reference 1.0000e+00 TeV 0.000e+00 nan nan True False
The model can be evaluated at given energies by calling the model instance:
[8]:
energy = [1, 3, 10, 30] * u.TeV
dnde = pwl(energy)
print(dnde)
[2.70000000e-12 2.40822469e-13 1.70358483e-14 1.51948705e-15] 1 / (cm2 s TeV)
The returned quantity is a differential photon flux.
For spectral models you can additionally compute the integrated and energy flux in a given energy range:
[9]:
flux = pwl.integral(energy_min=1 * u.TeV, energy_max=10 * u.TeV)
print(flux)
eflux = pwl.energy_flux(energy_min=1 * u.TeV, energy_max=10 * u.TeV)
print(eflux)
2.108034597491956e-12 1 / (cm2 s)
4.982075849517389e-12 TeV / (cm2 s)
This also works for a list or an array of integration boundaries:
[10]:
energy = [1, 3, 10, 30] * u.TeV
flux = pwl.integral(energy_min=energy[:-1], energy_max=energy[1:])
print(flux)
[1.64794383e-12 4.60090769e-13 1.03978226e-13] 1 / (cm2 s)
In some cases it can be useful to find use the inverse of a spectral model, to find the energy at which a given flux is reached:
[11]:
dnde = 2.7e-12 * u.Unit("TeV-1 cm-2 s-1")
energy = pwl.inverse(dnde)
print(energy)
1.0 TeV
As a convenience you can also plot any spectral model in a given energy range:
[12]:
pwl.plot(energy_bounds=[1, 100] * u.TeV)
[12]:
<AxesSubplot:xlabel='Energy [TeV]', ylabel='dnde [1 / (cm2 s TeV)]'>
Norm Spectral Models#
Normed spectral models are a special class of Spectral Models, which have a dimension-less normalisation. These spectral models feature a norm parameter instead of amplitude and are named using the NormSpectralModel suffix. They must be used along with another spectral model, as a multiplicative correction factor according to their spectral shape. They can be typically used for adjusting template based models, or adding a EBL correction to some analytic model.
To check if a given SpectralModel is a norm model, you can simply look at the is_norm_spectral_model property
[13]:
# To see the available norm models shipped with gammapy:
for model in SPECTRAL_MODEL_REGISTRY:
if model.is_norm_spectral_model:
print(model)
<class 'gammapy.modeling.models.spectral.PiecewiseNormSpectralModel'>
<class 'gammapy.modeling.models.spectral.EBLAbsorptionNormSpectralModel'>
<class 'gammapy.modeling.models.spectral.PowerLawNormSpectralModel'>
<class 'gammapy.modeling.models.spectral.LogParabolaNormSpectralModel'>
<class 'gammapy.modeling.models.spectral.ExpCutoffPowerLawNormSpectralModel'>
As an example, we see the PowerLawNormSpectralModel
[14]:
from gammapy.modeling.models import PowerLawNormSpectralModel
[15]:
pwl_norm = PowerLawNormSpectralModel(tilt=0.1)
print(pwl_norm)
PowerLawNormSpectralModel
type name value unit error min max frozen is_norm link
-------- --------- ---------- ---- --------- --- --- ------ ------- ----
spectral norm 1.0000e+00 0.000e+00 nan nan False True
spectral tilt 1.0000e-01 0.000e+00 nan nan True False
spectral reference 1.0000e+00 TeV 0.000e+00 nan nan True False
We can check the correction introduced at each energy
[16]:
energy = [0.3, 1, 3, 10, 30] * u.TeV
pwl_norm(energy)
[16]:
A typical use case of a norm model would be in applying spectral correction to a TemplateSpectralModel. A template model is defined by custom tabular values provided at initialization.
[17]:
from gammapy.modeling.models import TemplateSpectralModel
[18]:
energy = [0.3, 1, 3, 10, 30] * u.TeV
values = [40, 30, 20, 10, 1] * u.Unit("TeV-1 s-1 cm-2")
template = TemplateSpectralModel(energy, values)
template.plot(energy_bounds=[0.2, 50] * u.TeV, label="template model")
normed_template = template * pwl_norm
normed_template.plot(
energy_bounds=[0.2, 50] * u.TeV, label="normed_template model"
)
plt.legend();
Compound Spectral Model#
A CompoundSpectralModel is an arithmetic combination of two spectral models. The model normed_template created in the preceding example is an example of a CompoundSpectralModel
[19]:
print(normed_template)
CompoundSpectralModel
Component 1 : TemplateSpectralModel
type name value unit error min max frozen is_norm link
-------- ---- ---------- ---- --------- --- --- ------ ------- ----
spectral norm 1.0000e+00 0.000e+00 nan nan True True
Component 2 : PowerLawNormSpectralModel
type name value unit error min max frozen is_norm link
-------- --------- ---------- ---- --------- --- --- ------ ------- ----
spectral norm 1.0000e+00 0.000e+00 nan nan False True
spectral tilt 1.0000e-01 0.000e+00 nan nan True False
spectral reference 1.0000e+00 TeV 0.000e+00 nan nan True False
Operator : mul
To create an additive model, you can do simply:
[20]:
model_add = pwl + template
print(model_add)
CompoundSpectralModel
Component 1 : PowerLawSpectralModel
type name value unit error min max frozen is_norm link
-------- --------- ---------- -------------- --------- --- --- ------ ------- ----
spectral index 2.2000e+00 0.000e+00 nan nan False False
spectral amplitude 2.7000e-12 cm-2 s-1 TeV-1 0.000e+00 nan nan False True
spectral reference 1.0000e+00 TeV 0.000e+00 nan nan True False
Component 2 : TemplateSpectralModel
type name value unit error min max frozen is_norm link
-------- ---- ---------- ---- --------- --- --- ------ ------- ----
spectral norm 1.0000e+00 0.000e+00 nan nan True True
Operator : add
Spatial models#
Spatial models are imported from the same gammapy.modeling.models namespace, let’s start with a GaussianSpatialModel:
[21]:
from gammapy.modeling.models import GaussianSpatialModel
[22]:
gauss = GaussianSpatialModel(lon_0="0 deg", lat_0="0 deg", sigma="0.2 deg")
print(gauss)
GaussianSpatialModel
type name value unit error min max frozen is_norm link
------- ----- ---------- ---- --------- ---------- --------- ------ ------- ----
spatial lon_0 0.0000e+00 deg 0.000e+00 nan nan False False
spatial lat_0 0.0000e+00 deg 0.000e+00 -9.000e+01 9.000e+01 False False
spatial sigma 2.0000e-01 deg 0.000e+00 0.000e+00 nan False False
spatial e 0.0000e+00 0.000e+00 0.000e+00 1.000e+00 True False
spatial phi 0.0000e+00 deg 0.000e+00 nan nan True False
Again you can check the SPATIAL_MODELS registry to see which models are available or take a look at the model gallery.
[23]:
from gammapy.modeling.models import SPATIAL_MODEL_REGISTRY
print(SPATIAL_MODEL_REGISTRY)
Registry
--------
ConstantSpatialModel : ['ConstantSpatialModel', 'const']
TemplateSpatialModel : ['TemplateSpatialModel', 'template']
DiskSpatialModel : ['DiskSpatialModel', 'disk']
GaussianSpatialModel : ['GaussianSpatialModel', 'gauss']
GeneralizedGaussianSpatialModel: ['GeneralizedGaussianSpatialModel', 'gauss-general']
PointSpatialModel : ['PointSpatialModel', 'point']
ShellSpatialModel : ['ShellSpatialModel', 'shell']
Shell2SpatialModel : ['Shell2SpatialModel', 'shell2']
The default coordinate frame for all spatial models is "icrs", but the frame can be modified using the frame argument:
[24]:
gauss = GaussianSpatialModel(
lon_0="0 deg", lat_0="0 deg", sigma="0.2 deg", frame="galactic"
)
You can specify any valid astropy.coordinates frame. The center position of the model can be retrieved as a astropy.coordinates.SkyCoord object using SpatialModel.position:
[25]:
print(gauss.position)
<SkyCoord (Galactic): (l, b) in deg
(0., 0.)>
Spatial models can be evaluated again by calling the instance:
[26]:
lon = [0, 0.1] * u.deg
lat = [0, 0.1] * u.deg
flux_per_omega = gauss(lon, lat)
print(flux_per_omega)
[13061.88470839 10172.60603928] 1 / sr
The returned quantity corresponds to a surface brightness. Spatial model can be also evaluated using gammapy.maps.Map and gammapy.maps.Geom objects:
[27]:
m = Map.create(skydir=(0, 0), width=(1, 1), binsz=0.02, frame="galactic")
m.quantity = gauss.evaluate_geom(m.geom)
m.plot(add_cbar=True);
Again for convenience the model can be plotted directly:
[28]:
gauss.plot(add_cbar=True);
All spatial models have an associated sky region to it e.g. to illustrate the extend of the model on a sky image. The returned object is an regions.SkyRegion object:
[29]:
print(gauss.to_region())
Region: EllipseSkyRegion
center: <SkyCoord (Galactic): (l, b) in deg
(0., 0.)>
width: 0.6000000000000001 deg
height: 0.6000000000000001 deg
angle: 0.0 deg
Now we can plot the region on an sky image:
[30]:
# create and plot the model
gauss_elongated = GaussianSpatialModel(
lon_0="0 deg", lat_0="0 deg", sigma="0.2 deg", e=0.7, phi="45 deg"
)
ax = gauss_elongated.plot(add_cbar=True)
# add region illustration
region = gauss_elongated.to_region()
region_pix = region.to_pixel(ax.wcs)
ax.add_artist(region_pix.as_artist(ec="w", fc="None"));
The .to_region() method can also be useful to write e.g. ds9 region files using write_ds9 from the regions package:
[31]:
from regions import Regions
regions = Regions([gauss.to_region(), gauss_elongated.to_region()])
filename = "regions.reg"
regions.write(
filename,
format="ds9",
overwrite=True,
)
[32]:
!cat regions.reg
# Region file format: DS9 astropy/regions
fk5
ellipse(266.404996,-28.936172,0.300000,0.300000,0.000000)
ellipse(0.000006,0.000003,0.214243,0.300000,45.000000)
Temporal models#
Temporal models are imported from the same gammapy.modeling.models namespace, let’s start with a GaussianTemporalModel:
[33]:
from gammapy.modeling.models import GaussianTemporalModel
[34]:
gauss_temp = GaussianTemporalModel(t_ref=59240.0 * u.d, sigma=2.0 * u.d)
print(gauss_temp)
GaussianTemporalModel
type name value unit error min max frozen is_norm link
-------- ----- ---------- ---- --------- --- --- ------ ------- ----
temporal t_ref 5.9240e+04 d 0.000e+00 nan nan False False
temporal sigma 2.0000e+00 d 0.000e+00 nan nan False False
To check the TEMPORAL_MODELS registry to see which models are available:
[35]:
from gammapy.modeling.models import TEMPORAL_MODEL_REGISTRY
print(TEMPORAL_MODEL_REGISTRY)
Registry
--------
ConstantTemporalModel : ['ConstantTemporalModel', 'const']
LinearTemporalModel : ['LinearTemporalModel', 'linear']
LightCurveTemplateTemporalModel : ['LightCurveTemplateTemporalModel', 'template']
ExpDecayTemporalModel : ['ExpDecayTemporalModel', 'exp-decay']
GaussianTemporalModel : ['GaussianTemporalModel', 'gauss']
GeneralizedGaussianTemporalModel: ['GeneralizedGaussianTemporalModel', 'gengauss']
PowerLawTemporalModel : ['PowerLawTemporalModel', 'powerlaw']
SineTemporalModel : ['SineTemporalModel', 'sinus']
Temporal models can be evaluated on astropy.time.Time objects. The returned quantity is a dimensionless number
[36]:
from astropy.time import Time
time = Time("2021-01-29 00:00:00.000")
gauss_temp(time)
[36]:
As for other models, they can be plotted in a given time range
[37]:
time = Time([59233.0, 59250], format="mjd")
gauss_temp.plot(time)
[37]:
<AxesSubplot:xlabel='Time [iso]', ylabel='Norm / A.U.'>
SkyModel#
The gammapy.modeling.models.SkyModel class combines a spectral, and optionally, a spatial model and a temporal. It can be created from existing spectral, spatial and temporal model components:
[38]:
from gammapy.modeling.models import SkyModel
model = SkyModel(
spectral_model=pwl,
spatial_model=gauss,
temporal_model=gauss_temp,
name="my-source",
)
print(model)
SkyModel
Name : my-source
Datasets names : None
Spectral model type : PowerLawSpectralModel
Spatial model type : GaussianSpatialModel
Temporal model type : GaussianTemporalModel
Parameters:
index : 2.200 +/- 0.00
amplitude : 2.70e-12 +/- 0.0e+00 1 / (cm2 s TeV)
reference (frozen): 1.000 TeV
lon_0 : 0.000 +/- 0.00 deg
lat_0 : 0.000 +/- 0.00 deg
sigma : 0.200 +/- 0.00 deg
e (frozen): 0.000
phi (frozen): 0.000 deg
t_ref : 59240.000 +/- 0.00 d
sigma : 2.000 +/- 0.00 d
It is good practice to specify a name for your sky model, so that you can access it later by name and have meaningful identifier you serilisation. If you don’t define a name, a unique random name is generated:
[39]:
model_without_name = SkyModel(spectral_model=pwl, spatial_model=gauss)
print(model_without_name.name)
K98R2CDa
The individual components of the source model can be accessed using .spectral_model, .spatial_model and .temporal_model:
[40]:
model.spectral_model
[40]:
<gammapy.modeling.models.spectral.PowerLawSpectralModel at 0x15fa85be0>
[41]:
model.spatial_model
[41]:
<gammapy.modeling.models.spatial.GaussianSpatialModel at 0x15fec9df0>
[42]:
model.temporal_model
[42]:
<gammapy.modeling.models.temporal.GaussianTemporalModel at 0x168bec9d0>
And can be used as you have seen already seen above:
[43]:
model.spectral_model.plot(energy_bounds=[1, 10] * u.TeV);
Note that the gammapy fitting can interface only with a SkyModel and not its individual components. So, it is customary to work with SkyModel even if you are not doing a 3D fit. Since the amplitude parameter resides on the SpectralModel, specifying a spectral component is compulsory. The temporal and spatial components are optional. The temporal model needs to be specified only for timing analysis. In some cases (e.g. when doing a spectral analysis) there is no need for a spatial
component either, and only a spectral model is associated with the source.
[44]:
model_spectrum = SkyModel(spectral_model=pwl, name="source-spectrum")
print(model_spectrum)
SkyModel
Name : source-spectrum
Datasets names : None
Spectral model type : PowerLawSpectralModel
Spatial model type :
Temporal model type :
Parameters:
index : 2.200 +/- 0.00
amplitude : 2.70e-12 +/- 0.0e+00 1 / (cm2 s TeV)
reference (frozen): 1.000 TeV
Additionally the spatial model of gammapy.modeling.models.SkyModel can be used to represent source models based on templates, where the spatial and energy axes are correlated. It can be created e.g. from an existing FITS file:
[45]:
from gammapy.modeling.models import TemplateSpatialModel
from gammapy.modeling.models import PowerLawNormSpectralModel
[46]:
diffuse_cube = TemplateSpatialModel.read(
"$GAMMAPY_DATA/fermi-3fhl-gc/gll_iem_v06_gc.fits.gz", normalize=False
)
diffuse = SkyModel(PowerLawNormSpectralModel(), diffuse_cube)
print(diffuse)
SkyModel
Name : vZDhhooV
Datasets names : None
Spectral model type : PowerLawNormSpectralModel
Spatial model type : TemplateSpatialModel
Temporal model type :
Parameters:
norm : 1.000 +/- 0.00
tilt (frozen): 0.000
reference (frozen): 1.000 TeV
Note that if the spatial model is not normalized over the sky it has to be combined with a normalized spectral model, for example gammapy.modeling.models.PowerLawNormSpectralModel. This is the only case in gammapy.models.SkyModel where the unit is fully attached to the spatial model.
Modifying model parameters#
Model parameters can be modified (eg: frozen, values changed, etc at any point), eg:
[47]:
# Freezing a parameter
model.spectral_model.index.frozen = True
# Making a parameter free
model.spectral_model.index.frozen = False
[48]:
# Changing a value
model.spectral_model.index.value = 3
[49]:
# Setting min and max ranges on parameters
model.spectral_model.index.min = 1.0
model.spectral_model.index.max = 5.0
[50]:
# Visualise the model as a table
model.parameters.to_table().show_in_notebook()
[50]:
| idx | type | name | value | unit | error | min | max | frozen | is_norm | link |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | spectral | index | 3.0000e+00 | 0.000e+00 | 1.000e+00 | 5.000e+00 | False | False | ||
| 1 | spectral | amplitude | 2.7000e-12 | cm-2 s-1 TeV-1 | 0.000e+00 | nan | nan | False | True | |
| 2 | spectral | reference | 1.0000e+00 | TeV | 0.000e+00 | nan | nan | True | False | |
| 3 | spatial | lon_0 | 0.0000e+00 | deg | 0.000e+00 | nan | nan | False | False | |
| 4 | spatial | lat_0 | 0.0000e+00 | deg | 0.000e+00 | -9.000e+01 | 9.000e+01 | False | False | |
| 5 | spatial | sigma | 2.0000e-01 | deg | 0.000e+00 | 0.000e+00 | nan | False | False | |
| 6 | spatial | e | 0.0000e+00 | 0.000e+00 | 0.000e+00 | 1.000e+00 | True | False | ||
| 7 | spatial | phi | 0.0000e+00 | deg | 0.000e+00 | nan | nan | True | False | |
| 8 | temporal | t_ref | 5.9240e+04 | d | 0.000e+00 | nan | nan | False | False | |
| 9 | temporal | sigma | 2.0000e+00 | d | 0.000e+00 | nan | nan | False | False |
You can use the interactive boxes to choose model parameters by name, type or other attrributes mentioned in the column names.
Model lists and serialisation#
In a typical analysis scenario a model consists of multiple model components, or a “catalog” or “source library”. To handle this list of multiple model components, Gammapy has a Models class:
[51]:
from gammapy.modeling.models import Models
[52]:
models = Models([model, diffuse])
print(models)
Models
Component 0: SkyModel
Name : my-source
Datasets names : None
Spectral model type : PowerLawSpectralModel
Spatial model type : GaussianSpatialModel
Temporal model type : GaussianTemporalModel
Parameters:
index : 3.000 +/- 0.00
amplitude : 2.70e-12 +/- 0.0e+00 1 / (cm2 s TeV)
reference (frozen): 1.000 TeV
lon_0 : 0.000 +/- 0.00 deg
lat_0 : 0.000 +/- 0.00 deg
sigma : 0.200 +/- 0.00 deg
e (frozen): 0.000
phi (frozen): 0.000 deg
t_ref : 59240.000 +/- 0.00 d
sigma : 2.000 +/- 0.00 d
Component 1: SkyModel
Name : vZDhhooV
Datasets names : None
Spectral model type : PowerLawNormSpectralModel
Spatial model type : TemplateSpatialModel
Temporal model type :
Parameters:
norm : 1.000 +/- 0.00
tilt (frozen): 0.000
reference (frozen): 1.000 TeV
Individual model components in the list can be accessed by their name:
[53]:
print(models["my-source"])
SkyModel
Name : my-source
Datasets names : None
Spectral model type : PowerLawSpectralModel
Spatial model type : GaussianSpatialModel
Temporal model type : GaussianTemporalModel
Parameters:
index : 3.000 +/- 0.00
amplitude : 2.70e-12 +/- 0.0e+00 1 / (cm2 s TeV)
reference (frozen): 1.000 TeV
lon_0 : 0.000 +/- 0.00 deg
lat_0 : 0.000 +/- 0.00 deg
sigma : 0.200 +/- 0.00 deg
e (frozen): 0.000
phi (frozen): 0.000 deg
t_ref : 59240.000 +/- 0.00 d
sigma : 2.000 +/- 0.00 d
Note:To make the access by name unambiguous, models are required to have a unique name, otherwise an error will be thrown.
To see which models are available you can use the .names attribute:
[54]:
print(models.names)
['my-source', 'vZDhhooV']
Note that a SkyModel object can be evaluated for a given longitude, latitude, and energy, but the Models object cannot. This Models container object will be assigned to Dataset or Datasets together with the data to be fitted as explained in other analysis tutorials (see for example the modeling notebook).
The Models class also has in place .append() and .extend() methods:
[55]:
model_copy = model.copy(name="my-source-copy")
models.append(model_copy)
This list of models can be also serialised to a custom YAML based format:
[56]:
models_yaml = models.to_yaml()
print(models_yaml)
Template file already exits, and overwrite is False
components:
- name: my-source
type: SkyModel
spectral:
type: PowerLawSpectralModel
parameters:
- name: index
value: 3.0
is_norm: false
- name: amplitude
value: 2.7e-12
unit: cm-2 s-1 TeV-1
is_norm: true
- name: reference
value: 1.0
unit: TeV
frozen: true
is_norm: false
spatial:
type: GaussianSpatialModel
frame: galactic
parameters:
- name: lon_0
value: 0.0
unit: deg
is_norm: false
- name: lat_0
value: 0.0
unit: deg
is_norm: false
- name: sigma
value: 0.2
unit: deg
is_norm: false
- name: e
value: 0.0
frozen: true
is_norm: false
- name: phi
value: 0.0
unit: deg
frozen: true
is_norm: false
temporal:
type: GaussianTemporalModel
parameters:
- name: t_ref
value: 59240.0
unit: d
is_norm: false
- name: sigma
value: 2.0
unit: d
is_norm: false
- name: vZDhhooV
type: SkyModel
spectral:
type: PowerLawNormSpectralModel
parameters:
- name: norm
value: 1.0
is_norm: true
- name: tilt
value: 0.0
frozen: true
is_norm: false
- name: reference
value: 1.0
unit: TeV
frozen: true
is_norm: false
spatial:
type: TemplateSpatialModel
frame: galactic
parameters: []
filename: /Users/adonath/github/gammapy/gammapy-data/fermi-3fhl-gc/gll_iem_v06_gc.fits.gz
normalize: false
unit: 1 / (cm2 MeV s sr)
- name: my-source-copy
type: SkyModel
spectral:
type: PowerLawSpectralModel
parameters:
- name: index
value: 3.0
is_norm: false
- name: amplitude
value: 2.7e-12
unit: cm-2 s-1 TeV-1
is_norm: true
- name: reference
value: 1.0
unit: TeV
frozen: true
is_norm: false
spatial:
type: GaussianSpatialModel
frame: galactic
parameters:
- name: lon_0
value: 0.0
unit: deg
is_norm: false
- name: lat_0
value: 0.0
unit: deg
is_norm: false
- name: sigma
value: 0.2
unit: deg
is_norm: false
- name: e
value: 0.0
frozen: true
is_norm: false
- name: phi
value: 0.0
unit: deg
frozen: true
is_norm: false
temporal:
type: GaussianTemporalModel
parameters:
- name: t_ref
value: 59240.0
unit: d
is_norm: false
- name: sigma
value: 2.0
unit: d
is_norm: false
The structure of the yaml files follows the structure of the python objects. The components listed correspond to the SkyModel and SkyDiffuseCube components of the Models. For each SkyModel we have information about its name, type (corresponding to the tag attribute) and sub-mobels (i.e spectral model and eventually spatial model). Then the spatial and spectral models are defined by their type and parameters. The parameters keys name/value/unit are
mandatory, while the keys min/max/frozen are optionnals (so you can prepare shorter files).
If you want to write this list of models to disk and read it back later you can use:
[57]:
models.write("models.yaml", overwrite=True)
Template file already exits, and overwrite is False
[58]:
models_read = Models.read("models.yaml")
Additionally the models can exported and imported togeter with the data using the Datasets.read() and Datasets.write() methods as shown in the analysis_mwl notebook.
Implementing a custom model#
In order to add a user defined spectral model you have to create a SpectralModel subclass. This new model class should include:
a tag used for serialization (it can be the same as the class name)
an instantiation of each Parameter with their unit, default values and frozen status
the evaluate function where the mathematical expression for the model is defined.
As an example we will use a PowerLawSpectralModel plus a Gaussian (with fixed width). First we define the new custom model class that we name MyCustomSpectralModel:
[61]:
from gammapy.modeling import Parameter
from gammapy.modeling.models import SpectralModel
class MyCustomSpectralModel(SpectralModel):
"""My custom spectral model, parametrising a power law plus a Gaussian spectral line.
Parameters
----------
amplitude : `astropy.units.Quantity`
Amplitude of the spectra model.
index : `astropy.units.Quantity`
Spectral index of the model.
reference : `astropy.units.Quantity`
Reference energy of the power law.
mean : `astropy.units.Quantity`
Mean value of the Gaussian.
width : `astropy.units.Quantity`
Sigma width of the Gaussian line.
"""
tag = "MyCustomSpectralModel"
amplitude = Parameter(
"amplitude", "1e-12 cm-2 s-1 TeV-1", min=0, is_norm=True
)
index = Parameter("index", 2, min=0)
reference = Parameter("reference", "1 TeV", frozen=True)
mean = Parameter("mean", "1 TeV", min=0)
width = Parameter("width", "0.1 TeV", min=0, frozen=True)
@staticmethod
def evaluate(energy, index, amplitude, reference, mean, width):
pwl = PowerLawSpectralModel.evaluate(
energy=energy,
index=index,
amplitude=amplitude,
reference=reference,
)
gauss = amplitude * np.exp(-((energy - mean) ** 2) / (2 * width**2))
return pwl + gauss
It is good practice to also implement a docstring for the model, defining the parameters and also definig a tag, which specifies the name of the model for serialisation. Also note that gammapy assumes that all SpectralModel evaluate functions return a flux in unit of "cm-2 s-1 TeV-1" (or equivalent dimensions).
This model can now be used as any other spectral model in Gammapy:
[62]:
my_custom_model = MyCustomSpectralModel(mean="3 TeV")
print(my_custom_model)
MyCustomSpectralModel
type name value unit error min max frozen is_norm link
-------- --------- ---------- -------------- --------- --------- --- ------ ------- ----
spectral amplitude 1.0000e-12 cm-2 s-1 TeV-1 0.000e+00 0.000e+00 nan False True
spectral index 2.0000e+00 0.000e+00 0.000e+00 nan False False
spectral reference 1.0000e+00 TeV 0.000e+00 nan nan True False
spectral mean 3.0000e+00 TeV 0.000e+00 0.000e+00 nan False False
spectral width 1.0000e-01 TeV 0.000e+00 0.000e+00 nan True False
[63]:
my_custom_model.integral(1 * u.TeV, 10 * u.TeV)
[63]:
[64]:
my_custom_model.plot(energy_bounds=[1, 10] * u.TeV)
[64]:
<AxesSubplot:xlabel='Energy [TeV]', ylabel='dnde [1 / (cm2 s TeV)]'>
As a next step we can also register the custom model in the SPECTRAL_MODELS registry, so that it becomes available for serilisation:
[65]:
SPECTRAL_MODEL_REGISTRY.append(MyCustomSpectralModel)
[66]:
model = SkyModel(spectral_model=my_custom_model, name="my-source")
models = Models([model])
models.write("my-custom-models.yaml", overwrite=True)
[67]:
!cat my-custom-models.yaml
components:
- name: my-source
type: SkyModel
spectral:
type: MyCustomSpectralModel
parameters:
- name: amplitude
value: 1.0e-12
unit: cm-2 s-1 TeV-1
is_norm: true
- name: index
value: 2.0
is_norm: false
- name: reference
value: 1.0
unit: TeV
frozen: true
is_norm: false
- name: mean
value: 3.0
unit: TeV
is_norm: false
- name: width
value: 0.1
unit: TeV
frozen: true
is_norm: false
covariance: my-custom-models_covariance.dat
Similarly you can also create custom spatial models and add them to the SPATIAL_MODELS registry. In that case gammapy assumes that the evaluate function return a normalized quantity in “sr-1” such as the model integral over the whole sky is one.
Models with energy dependent morphology#
A common science case in the study of extended sources is to probe for energy dependent morphology, eg: in Supernova Remnants or Pulsar Wind Nebulae. Traditionally, this has been done by splitting the data into energy bands and doing individual fits of the morphology in these energy bands.
SkyModel offers a natural framework to simultaneously model the energy and morphology, e.g. spatial extent described by a parametric model expression with energy dependent parameters.
The models shipped within gammapy use a “factorised” representation of the source model, where the spatial (\(l,b\)), energy (\(E\)) and time (\(t\)) dependence are independent model components and not correlated:
To use full 3D models, ie $f(l, b, E) = F(l, b, E) \cdot `G(E) $, you have to implement your own custom ``SpatialModel`. Note that it is still necessary to multiply by a SpectralModel, \(G(E)\) to be dimensionally consistent.
In this example, we create Gaussian Spatial Model with the extension varying with energy. For simplicity, we assume a linear dependence on energy and parameterize this by specifying the extension at 2 energies. You can add more complex dependences, probably motivated by physical models.
[68]:
from gammapy.modeling.models import SpatialModel
from astropy.coordinates.angle_utilities import angular_separation
class MyCustomGaussianModel(SpatialModel):
"""My custom Energy Dependent Gaussian model.
Parameters
----------
lon_0, lat_0 : `~astropy.coordinates.Angle`
Center position
sigma_1TeV : `~astropy.coordinates.Angle`
Width of the Gaussian at 1 TeV
sigma_10TeV : `~astropy.coordinates.Angle`
Width of the Gaussian at 10 TeV
"""
tag = "MyCustomGaussianModel"
is_energy_dependent = True
lon_0 = Parameter("lon_0", "0 deg")
lat_0 = Parameter("lat_0", "0 deg", min=-90, max=90)
sigma_1TeV = Parameter("sigma_1TeV", "2.0 deg", min=0)
sigma_10TeV = Parameter("sigma_10TeV", "0.2 deg", min=0)
@staticmethod
def evaluate(lon, lat, energy, lon_0, lat_0, sigma_1TeV, sigma_10TeV):
sep = angular_separation(lon, lat, lon_0, lat_0)
# Compute sigma for the given energy using linear interpolation in log energy
sigma_nodes = u.Quantity([sigma_1TeV, sigma_10TeV])
energy_nodes = [1, 10] * u.TeV
log_s = np.log(sigma_nodes.to("deg").value)
log_en = np.log(energy_nodes.to("TeV").value)
log_e = np.log(energy.to("TeV").value)
sigma = np.exp(np.interp(log_e, log_en, log_s)) * u.deg
exponent = -0.5 * (sep / sigma) ** 2
norm = 1 / (2 * np.pi * sigma**2)
return norm * np.exp(exponent)
Serialisation of this model can be achieved as explained in the previous section. You can now use it as stadard SpatialModel in your analysis. Note that this is still a SpatialModel, and not a SkyModel, so it needs to be multiplied by a SpectralModel as before.
[69]:
spatial_model = MyCustomGaussianModel()
spectral_model = PowerLawSpectralModel()
sky_model = SkyModel(
spatial_model=spatial_model, spectral_model=spectral_model
)
[70]:
spatial_model.evaluation_radius
To visualise it, we evaluate it on a 3D geom.
[71]:
energy_axis = MapAxis.from_energy_bounds(
energy_min=0.1 * u.TeV, energy_max=10.0 * u.TeV, nbin=3, name="energy_true"
)
geom = WcsGeom.create(
skydir=(0, 0), width=5.0 * u.deg, binsz=0.1, axes=[energy_axis]
)
spatial_model.plot_grid(geom=geom, add_cbar=True, figsize=(14, 3));
For computational purposes, it is useful to specify a evaluation_radius for SpatialModels - this gives a size on which to compute the model. Though optional, it is highly recommended for Custom Spatial Models. This can be done, for ex, by defining the following function inside the above class:
[72]:
@property
def evaluation_radius(self):
"""Evaluation radius (`~astropy.coordinates.Angle`)."""
return 5 * np.max([self.sigma_1TeV.value, self.sigma_10TeV.value]) * u.deg