FluxPointsDataset#

class gammapy.datasets.FluxPointsDataset(models=None, data=None, mask_fit=None, mask_safe=None, name=None, meta_table=None, stat_type='chi2', stat_kwargs=None)[source]#

Bases: Dataset

Bundle a set of flux points with a parametric model, to compute fit statistic function using different statistics (see stat_type).

For more information see Datasets (DL4).

Parameters:

modelsModels

Models (only spectral part needs to be set).

dataFluxPoints

Flux points. Must be sorted along the energy axis.

mask_fitnumpy.ndarray

Mask to apply for fitting.

mask_safenumpy.ndarray

Mask defining the safe data range. By default, upper limit values are excluded.

meta_tableTable

Table listing information on observations used to create the dataset. One line per observation for stacked datasets.

stat_typestr

Method used to compute the statistics:

chi2 : estimate from chi2 statistics.

profile : estimate from interpolation of the likelihood profile.

distrib : Assuming gaussian errors the likelihood is given by the probability density function of the normal distribution. For the upper limit case it is necessary to marginalize over the unknown measurement, so we integrate the normal distribution up to the upper limit value which gives the complementary error function. See eq. C7 of Mohanty et al (2013)

Default is chi2, in that case upper limits are ignored and the mean of asymetrics error is used. However, it is recommended to use profile if stat_scan is available on flux points. The distrib case provides an approximation if the profile is not available.

stat_kwargsdict

Extra arguments specifying the interpolation scheme of the likelihood profile. Used only if stat_type=="profile". In that case the default is : stat_kwargs={"interp_scale":"sqrt", "extrapolate":True}

Examples

Load flux points from file and fit with a power-law model:

>>> from gammapy.modeling import Fit
>>> from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
>>> from gammapy.estimators import FluxPoints
>>> from gammapy.datasets import FluxPointsDataset
>>> filename = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
>>> dataset = FluxPointsDataset.read(filename)
>>> model = SkyModel(spectral_model=PowerLawSpectralModel())
>>> dataset.models = model

Make the fit:

>>> fit = Fit()
>>> result = fit.run([dataset])
>>> print(result)
OptimizeResult

    backend    : minuit
    method     : migrad
    success    : True
    message    : Optimization terminated successfully.
    nfev       : 135
    total stat : 25.21

CovarianceResult

    backend    : minuit
    method     : hesse
    success    : True
    message    : Hesse terminated successfully.

>>> print(result.parameters.to_table())
type    name     value         unit      ... frozen link prior
---- --------- ---------- -------------- ... ------ ---- -----
         index 2.2159e+00                ...  False
     amplitude 2.1619e-13 TeV-1 s-1 cm-2 ...  False
     reference 1.0000e+00            TeV ...   True

Note: In order to reproduce the example, you need the tests datasets folder. You may download it with the command: gammapy download datasets --tests --out $GAMMAPY_DATA

Attributes Summary

`available_stat_type`
`gti`	Good time interval info (`GTI`).
`mask`	Combined fit and safe mask.
`mask_safe`
`mask_valid`
`models`
`name`
`stat_type`
`tag`

Methods Summary

`copy`([name])	A deep copy.
`data_shape`()	Shape of the flux points data (tuple).
`flux_pred`()	Compute predicted flux.
`from_dict`(data, **kwargs)	Create flux point dataset from dict.
`plot_fit`([ax_spectrum, ax_residuals, ...])	Plot flux points, best fit model and residuals in two panels.
`plot_residuals`([ax, method])	Plot flux point residuals.
`plot_spectrum`([ax, kwargs_fp, kwargs_model, ...])	Plot flux points and model.
`read`(filename[, name])	Read pre-computed flux points and create a dataset.
`residuals`([method])	Compute flux point residuals.
`stat_array`()	Fit statistic array.
`stat_sum`()	Total statistic given the current model parameters and priors.
`to_dict`()	Convert to dict for YAML serialization.
`write`(filename[, overwrite, checksum])	Write flux point dataset to file.

Attributes Documentation

available_stat_type#

gti#: Good time interval info (GTI).

mask#: Combined fit and safe mask.

mask_safe#

mask_valid#

models#

name#

stat_type#

tag = 'FluxPointsDataset'#

Methods Documentation

copy(name=None)#

A deep copy.

Parameters:

namestr, optional: Name of the copied dataset. Default is None.

Returns:

datasetDataset: Copied datasets.

data_shape()[source]#: Shape of the flux points data (tuple).

flux_pred()[source]#: Compute predicted flux.

classmethod from_dict(data, **kwargs)[source]#

Create flux point dataset from dict.

Parameters:

datadict: Dictionary containing data to create dataset from.
Returns
——-
datasetFluxPointsDataset: Flux point datasets.

plot_fit(ax_spectrum=None, ax_residuals=None, kwargs_spectrum=None, kwargs_residuals=None)[source]#

Plot flux points, best fit model and residuals in two panels.

Calls plot_spectrum and plot_residuals.

Parameters:

ax_spectrumAxes, optional: Axes to plot flux points and best fit model on. Default is None.
ax_residualsAxes, optional: Axes to plot residuals on. Default is None.
kwargs_spectrumdict, optional: Keyword arguments passed to plot_spectrum. Default is None.
kwargs_residualsdict, optional: Keyword arguments passed to plot_residuals. Default is None.

Returns:

ax_spectrum, ax_residualsAxes: Flux points, best fit model and residuals plots.

Examples

>>> from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
>>> from gammapy.estimators import FluxPoints
>>> from gammapy.datasets import FluxPointsDataset

>>> #load precomputed flux points
>>> filename = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
>>> flux_points = FluxPoints.read(filename)
>>> model = SkyModel(spectral_model=PowerLawSpectralModel())
>>> dataset = FluxPointsDataset(model, flux_points)
>>> #configuring optional parameters
>>> kwargs_spectrum = {"kwargs_model": {"color":"red", "ls":"--"}, "kwargs_fp":{"color":"green", "marker":"o"}}
>>> kwargs_residuals = {"color": "blue", "markersize":4, "marker":'s', }
>>> dataset.plot_fit(kwargs_residuals=kwargs_residuals, kwargs_spectrum=kwargs_spectrum) 

plot_residuals(ax=None, method='diff', **kwargs)[source]#

Plot flux point residuals.

Parameters:

axAxes, optional: Axes to plot on. Default is None.
method{“diff”, “diff/model”}: Normalization used to compute the residuals, see FluxPointsDataset.residuals. Default is “diff”.
**kwargsdict: Keyword arguments passed to errorbar.

Returns:

axAxes: Axes object.

plot_spectrum(ax=None, kwargs_fp=None, kwargs_model=None, axis_name='energy')[source]#

Plot flux points and model.

Parameters:

axAxes, optional: Axes to plot on. Default is None.
kwargs_fpdict, optional: Keyword arguments passed to gammapy.estimators.FluxPoints.plot to configure the plot style. Default is None.
kwargs_modeldict, optional: Keyword arguments passed to gammapy.modeling.models.SpectralModel.plot and gammapy.modeling.models.SpectralModel.plot_error to configure the plot style. Default is None.
axis_namestr: Axis along which to plot the flux points for multiple axes. Default is energy.

Returns:

axAxes: Axes object.

Examples

>>> from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
>>> from gammapy.estimators import FluxPoints
>>> from gammapy.datasets import FluxPointsDataset

>>> #load precomputed flux points
>>> filename = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
>>> flux_points = FluxPoints.read(filename)
>>> model = SkyModel(spectral_model=PowerLawSpectralModel())
>>> dataset = FluxPointsDataset(model, flux_points)
>>> #configuring optional parameters
>>> kwargs_model = {"color":"red", "ls":"--"}
>>> kwargs_fp = {"color":"green", "marker":"o"}
>>> dataset.plot_spectrum(kwargs_fp=kwargs_fp, kwargs_model=kwargs_model) 

classmethod read(filename, name=None)[source]#

Read pre-computed flux points and create a dataset.

Parameters:

filenamestr: Filename to read from.
namestr, optional: Name of the new dataset. Default is None.
Returns
——-
datasetFluxPointsDataset: FluxPointsDataset.

residuals(method='diff')[source]#

Compute flux point residuals.

Parameters:

method: {“diff”, “diff/model”}

Method used to compute the residuals. Available options are:

"diff" (default): data - model.
"diff/model": (data - model) / model.

Returns:

residualsndarray: Residuals array.

stat_array()[source]#: Fit statistic array.

stat_sum()#: Total statistic given the current model parameters and priors.

to_dict()#: Convert to dict for YAML serialization.

write(filename, overwrite=False, checksum=False, **kwargs)[source]#

Write flux point dataset to file.

Parameters:

filenamestr: Filename to write to.
overwritebool, optional: Overwrite existing file. Default is False.
checksumbool: When True adds both DATASUM and CHECKSUM cards to the headers written to the FITS file. Applies only if filename has .fits suffix. Default is False.
**kwargsdict, optional: Keyword arguments passed to write.

FluxPointsDataset#

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