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Pull Request — main (#160)

by Chaitanya

created 2024-01-23 23:34 UTC

asgardpy.stats.stats.fetch_pivot_energy() A

↳ Parent: asgardpy.stats.stats

Complexity

Conditions

Size

Total Lines	36
Code Lines	13

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	3
eloc	13
nop	1
dl	0
loc	36
rs	9.75
c	0
b	0
f	0

"""
Module for performing some statistic functions.
"""
import numpy as np
from gammapy.modeling.models import CompoundSpectralModel
from gammapy.stats.fit_statistics import cash, wstat
from scipy.optimize import minimize_scalar
from scipy.stats import chi2, norm

__all__ = [
    "check_model_preference_aic",
    "check_model_preference_lrt",
    "fetch_pivot_energy",
    "get_chi2_sig_pval",
    "get_goodness_of_fit_stats",
    "get_ts_target",
]


def get_chi2_sig_pval(test_stat, ndof):
    """
    Using the log-likelihood value for a model fitting to data, along with the
    total degrees of freedom, evaluate the significance value in terms of gaussian
    distribution along with one-tailed p-value for the fitting statistics.

    In Gammapy, for 3D analysis, cash statistics is used, while for 1D analysis,
    wstat statistics is used. Check the documentation for more details
    https://docs.gammapy.org/1.1/user-guide/stats/index.html

    Parameters
    ----------
    test_stat: float
        The test statistic (-2 ln L) value of the fitting.
    ndof: int
        Total number of degrees of freedom.

    Returns
    -------
    chi2_sig: float
        significance (Chi2) of the likelihood of fit model estimated in
        Gaussian distribution.
    pval: float
        p-value for the model fitting

    """
    pval = chi2.sf(test_stat, ndof)
    chi2_sig = norm.isf(pval / 2)

    return chi2_sig, pval


def check_model_preference_lrt(test_stat_1, test_stat_2, ndof_1, ndof_2):
    """
    Log-likelihood ratio test. Checking the preference of a "nested" spectral
    model2 (observed), over a primary model1.

    Parameters
    ----------
    test_stat_1: float
        The test statistic (-2 ln L) of the Fit result of the primary spectral model.
    test_stat_2: float
        The test statistic (-2 ln L) of the Fit result of the nested spectral model.
    ndof_1: int
        Number of degrees of freedom for the primary model
    ndof_2: int
        Number of degrees of freedom for the nested model

    Returns
    -------
    p_value: float
        p-value for the ratio of the likelihoods
    gaussian_sigmas: float
        significance (Chi2) of the ratio of the likelihoods estimated in
        Gaussian distribution.
    n_dof: int
        number of degrees of freedom or free parameters between primary and
        nested model.
    """
    n_dof = ndof_1 - ndof_2

    if n_dof < 1:
        print(f"DoF is lower in {ndof_1} compared to {ndof_2}")

        return np.nan, np.nan, n_dof

    gaussian_sigmas, p_value = get_chi2_sig_pval(test_stat_1 - test_stat_2, n_dof)

    return p_value, gaussian_sigmas, n_dof


def check_model_preference_aic(list_stat, list_dof):
    """
    Akaike Information Criterion (AIC) preference over a list of stat and DoF
    (degree of freedom) to get relative likelihood of a given list of best-fit
    models.

    Parameters
    ----------
    list_wstat: list
        List of stat or -2 Log likelihood values for a list of models.
    list_dof: list
        List of degrees of freedom or list of free parameters, for a list of models.

    Returns
    -------
    list_rel_p: list
        List of relative likelihood probabilities, for a list of models.
    """
    list_aic_stat = []
    for stat, dof in zip(list_stat, list_dof, strict=True):
        aic_stat = stat + 2 * dof
        list_aic_stat.append(aic_stat)
    list_aic_stat = np.array(list_aic_stat)

    aic_stat_min = np.min(list_aic_stat)

    list_b_stat = []
    for aic in list_aic_stat:
        b_stat = np.exp((aic_stat_min - aic) / 2)
        list_b_stat.append(b_stat)
    list_b_stat = np.array(list_b_stat)

    list_rel_p = []
    for b_stat in list_b_stat:
        rel_p = b_stat / np.sum(list_b_stat)
        list_rel_p.append(rel_p)
    list_rel_p = np.array(list_rel_p)

    return list_rel_p


def get_goodness_of_fit_stats(datasets, instrument_spectral_info):
    """
    Evaluating the Goodness of Fit statistics of the fitting of the model to
    the dataset.

    We first use the get_ts_target function to get the total test statistic for
    the (observed) best fit of the model to the data, and the (expected)
    perfect fit of model and data (model = data), for the given target source
    region/pixel.

    We then evaluate the total number of Degrees of Freedom for the Fit as the
    difference between the number of relevant energy bins used in the evaluation
    and the number of free model parameters.

    The fit statistics difference is used as the test statistic value for
    get_chi2_sig_pval function along with the total number of degrees of freedom
    to get the final statistics for the goodness of fit.

    The fit statistics information is updated in the dict object provided and
    a logging message is passed.

    Parameter
    ---------
    datasets: `gammapy.datasets.Datasets`
        List of Datasets object, which can contain 3D and/or 1D datasets
    instrument_spectral_info: dict
        Dict of information for storing relevant fit stats

    Return
    ------
    instrument_spectral_info: dict
        Filled Dict of information with relevant fit statistics
    stat_message: str
        String for logging the fit statistics
    """
    stat_best_fit, stat_max_fit = get_ts_target(datasets)

    instrument_spectral_info["max_fit_stat"] = stat_max_fit
    instrument_spectral_info["best_fit_stat"] = stat_best_fit
    ndof = instrument_spectral_info["DoF"]
    stat_diff_gof = stat_best_fit - stat_max_fit

    fit_chi2_sig, fit_pval = get_chi2_sig_pval(stat_diff_gof, ndof)

    instrument_spectral_info["fit_chi2_sig"] = fit_chi2_sig
    instrument_spectral_info["fit_pval"] = fit_pval

    stat_message = "The Chi2/dof value of the goodness of Fit is "
    stat_message += f"{stat_diff_gof:.2f}/{ndof}\nand the p-value is {fit_pval:.3e} "
    stat_message += f"and in Significance {fit_chi2_sig:.2f} sigmas"
    stat_message += f"\nwith best fit TS (Observed) as {stat_best_fit:.3f} "
    stat_message += f"and max fit TS (Expected) as {stat_max_fit:.3f}"

    return instrument_spectral_info, stat_message


def get_ts_target(datasets):
    """
    From a given list of DL4 datasets, with assumed associated models, estimate
    the total test statistic values, in the given target source region/pixel,
    for the (observed) best fit of the model to the data, and the (expected)
    perfect fit of model and data (model = data).

    For consistency in the evaluation of the statistic values, we will use the
    basic Fit Statistic functions in Gammapy for Poisson Data:

    * `cash <https://docs.gammapy.org/1.1/api/gammapy.stats.cash.html>`_

    * `wstat <https://docs.gammapy.org/1.1/api/gammapy.stats.wstat.html>`_

    For the different type of Statistics used in Gammapy for 3D/1D datasets,
    and for our use case of getting the best fit and perfect fit, we will pass
    the appropriate values, by adapting to the following methods,

    * Best Fit (Observed):

        * `Cash stat_array <https://docs.gammapy.org/1.1/api/gammapy.datasets.MapDataset.html#gammapy.datasets.MapDataset.stat_array # noqa>`_

        * `Wstat stat_array <https://docs.gammapy.org/1.1/api/gammapy.datasets.MapDatasetOnOff.html#gammapy.datasets.MapDatasetOnOff.stat_array # noqa>`_

    * Perfect Fit (Expected):

        * `Cash stat_max <https://docs.gammapy.org/1.1/api/gammapy.stats.CashCountsStatistic.html#gammapy.stats.CashCountsStatistic.stat_max # noqa>`_

        * `Wstat stat_max <https://docs.gammapy.org/1.1/api/gammapy.stats.WStatCountsStatistic.html#gammapy.stats.WStatCountsStatistic.stat_max # noqa>`_

    Parameter
    ---------
    datasets: `gammapy.datasets.Datasets`
        List of Datasets object, which can contain 3D and/or 1D datasets

    Return
    ------
    stat_best_fit: float
        Total sum of test statistic of the best fit of model to data, summed
        over all energy bins.
    stat_max_fit: float
        Test statistic difference of the perfect fit of model to data summed
        over all energy bins.
    """  # noqa
    stat_best_fit = 0
    stat_max_fit = 0

    for data in datasets:
        if data.stat_type != "chi2":
            # Assuming that the Counts Map is created with the target source as its center
            region = data.counts.geom.center_skydir

            if data.stat_type == "cash":
                counts_on = (data.counts.copy() * data.mask).get_spectrum(region).data
                mu_on = (data.npred() * data.mask).get_spectrum(region).data

                stat_best_fit += np.nansum(cash(n_on=counts_on, mu_on=mu_on).ravel())
                stat_max_fit += np.nansum(cash(n_on=counts_on, mu_on=counts_on).ravel())

            elif data.stat_type == "wstat":
                counts_on = (data.counts.copy() * data.mask).get_spectrum(region).data
                counts_off = np.nan_to_num((data.counts_off * data.mask).get_spectrum(region)).data

                # alpha is evaluated by acceptance ratios, and
                # Background is evaluated with given alpha and counts_off,
                # but for alpha to be of the same shape (in the target region),
                # it will be reevaluated
                bkg = np.nan_to_num((data.background * data.mask).get_spectrum(region))

                with np.errstate(invalid="ignore", divide="ignore"):
                    alpha = bkg / counts_off
                mu_signal = np.nan_to_num((data.npred_signal() * data.mask).get_spectrum(region)).data
                max_pred = counts_on - bkg

                stat_best_fit += np.nansum(wstat(n_on=counts_on, n_off=counts_off, alpha=alpha, mu_sig=mu_signal))
                stat_max_fit += np.nansum(wstat(n_on=counts_on, n_off=counts_off, alpha=alpha, mu_sig=max_pred))
        else:
            # For FluxxPointsDataset
            stat_best_fit += np.nansum(data.stat_array())
            stat_max_fit += len(data.data.dnde.data)

    return stat_best_fit, stat_max_fit


def pivot_energy(spectral_model):
    """
    Using function of SpectralModel object in Gammapy 1.2.dev build. Will be removed
    with the new Gammapy release.

    Pivot or decorrelation energy, for a given spectral model calculated numerically.

    It is defined as the energy at which the correlation between the spectral parameters is minimized.

    Returns
    -------
    pivot energy : `~astropy.units.Quantity`
        The energy at which the statistical error in the computed flux is smallest.
        If no minimum is found, NaN will be returned.
    """
    x_unit = spectral_model.reference.unit

    def min_func(x):
        """Function to minimise."""
        x = np.exp(x)
        dnde, dnde_error = spectral_model.evaluate_error(x * x_unit)
        return dnde_error / dnde

    bounds = [np.log(spectral_model.reference.value) - 3, np.log(spectral_model.reference.value) + 3]

    std = np.std(min_func(x=np.linspace(bounds[0], bounds[1], 100)))
    if std < 1e-5:
        print("The relative error on the flux does not depend on energy. No pivot energy found.")
        return np.nan * x_unit

    minimizer = minimize_scalar(min_func, bounds=bounds)

    if not minimizer.success:
        print("No minima found in the relative error on the flux. Pivot energy computation failed.")
        return np.nan * x_unit
    else:
        return np.exp(minimizer.x) * x_unit


def fetch_pivot_energy(analysis):
    """
    Using an 'AsgardpyAnalysis' object to get the pivot energy for a given dataset
    and fit model, using the pivot_energy function.

    In Gammapy v1.2, use instead
    'analysis.fit_result.models[0].spectral_model.model1.pivot_energy' to get this value.

    Returns
    -------
    pivot energy : `~astropy.units.Quantity`
        The energy at which the statistical error in the computed flux is smallest.
        If no minimum is found, NaN will be returned.
    """
    # Check if DL4 datasets are created, and if not, only run steps till Fit
    if len(analysis.datasets) == 0:
        steps = [step for step in analysis.config.general.steps if step != "flux-points"]

        analysis.run(steps)
    else:
        analysis.run(["fit"])

    # Assuming EBL model is present
    if isinstance(analysis.datasets[0].models[0].spectral_model, CompoundSpectralModel):
        temp_model = analysis.datasets[0].models[0].spectral_model.model1
    else:
        temp_model = analysis.datasets[0].models[0].spectral_model

    # Fetching the covariance matrix for the given dataset and optimized fit model
    cov_matrix = analysis.fit.covariance(
        datasets=analysis.datasets, optimize_result=analysis.fit_result.optimize_result
    ).matrix

    temp_model.covariance = cov_matrix[: len(temp_model.parameters), : len(temp_model.parameters)]

    return pivot_energy(temp_model)


1			"""
2			Module for performing some statistic functions.
3			"""
4			import numpy as np
5			from gammapy.modeling.models import CompoundSpectralModel
6			from gammapy.stats.fit_statistics import cash, wstat
7			from scipy.optimize import minimize_scalar
8			from scipy.stats import chi2, norm
9
10			__all__ = [
11			"check_model_preference_aic",
12			"check_model_preference_lrt",
13			"fetch_pivot_energy",
14			"get_chi2_sig_pval",
15			"get_goodness_of_fit_stats",
16			"get_ts_target",
17			]
18
19
20			def get_chi2_sig_pval(test_stat, ndof):
21			"""
22			Using the log-likelihood value for a model fitting to data, along with the
23			total degrees of freedom, evaluate the significance value in terms of gaussian
24			distribution along with one-tailed p-value for the fitting statistics.
25
26			In Gammapy, for 3D analysis, cash statistics is used, while for 1D analysis,
27			wstat statistics is used. Check the documentation for more details
28			https://docs.gammapy.org/1.1/user-guide/stats/index.html
29
30			Parameters
31			----------
32			test_stat: float
33			The test statistic (-2 ln L) value of the fitting.
34			ndof: int
35			Total number of degrees of freedom.
36
37			Returns
38			-------
39			chi2_sig: float
40			significance (Chi2) of the likelihood of fit model estimated in
41			Gaussian distribution.
42			pval: float
43			p-value for the model fitting
44
45			"""
46			pval = chi2.sf(test_stat, ndof)
47			chi2_sig = norm.isf(pval / 2)
48
49			return chi2_sig, pval
50
51
52			def check_model_preference_lrt(test_stat_1, test_stat_2, ndof_1, ndof_2):
53			"""
54			Log-likelihood ratio test. Checking the preference of a "nested" spectral
55			model2 (observed), over a primary model1.
56
57			Parameters
58			----------
59			test_stat_1: float
60			The test statistic (-2 ln L) of the Fit result of the primary spectral model.
61			test_stat_2: float
62			The test statistic (-2 ln L) of the Fit result of the nested spectral model.
63			ndof_1: int
64			Number of degrees of freedom for the primary model
65			ndof_2: int
66			Number of degrees of freedom for the nested model
67
68			Returns
69			-------
70			p_value: float
71			p-value for the ratio of the likelihoods
72			gaussian_sigmas: float
73			significance (Chi2) of the ratio of the likelihoods estimated in
74			Gaussian distribution.
75			n_dof: int
76			number of degrees of freedom or free parameters between primary and
77			nested model.
78			"""
79			n_dof = ndof_1 - ndof_2
80
81			if n_dof < 1:
82			print(f"DoF is lower in {ndof_1} compared to {ndof_2}")
83
84			return np.nan, np.nan, n_dof
85
86			gaussian_sigmas, p_value = get_chi2_sig_pval(test_stat_1 - test_stat_2, n_dof)
87
88			return p_value, gaussian_sigmas, n_dof
89
90
91			def check_model_preference_aic(list_stat, list_dof):
92			"""
93			Akaike Information Criterion (AIC) preference over a list of stat and DoF
94			(degree of freedom) to get relative likelihood of a given list of best-fit
95			models.
96
97			Parameters
98			----------
99			list_wstat: list
100			List of stat or -2 Log likelihood values for a list of models.
101			list_dof: list
102			List of degrees of freedom or list of free parameters, for a list of models.
103
104			Returns
105			-------
106			list_rel_p: list
107			List of relative likelihood probabilities, for a list of models.
108			"""
109			list_aic_stat = []
110			for stat, dof in zip(list_stat, list_dof, strict=True):
111			aic_stat = stat + 2 * dof
112			list_aic_stat.append(aic_stat)
113			list_aic_stat = np.array(list_aic_stat)
114
115			aic_stat_min = np.min(list_aic_stat)
116
117			list_b_stat = []
118			for aic in list_aic_stat:
119			b_stat = np.exp((aic_stat_min - aic) / 2)
120			list_b_stat.append(b_stat)
121			list_b_stat = np.array(list_b_stat)
122
123			list_rel_p = []
124			for b_stat in list_b_stat:
125			rel_p = b_stat / np.sum(list_b_stat)
126			list_rel_p.append(rel_p)
127			list_rel_p = np.array(list_rel_p)
128
129			return list_rel_p
130
131
132			def get_goodness_of_fit_stats(datasets, instrument_spectral_info):
133			"""
134			Evaluating the Goodness of Fit statistics of the fitting of the model to
135			the dataset.
136
137			We first use the get_ts_target function to get the total test statistic for
138			the (observed) best fit of the model to the data, and the (expected)
139			perfect fit of model and data (model = data), for the given target source
140			region/pixel.
141
142			We then evaluate the total number of Degrees of Freedom for the Fit as the
143			difference between the number of relevant energy bins used in the evaluation
144			and the number of free model parameters.
145
146			The fit statistics difference is used as the test statistic value for
147			get_chi2_sig_pval function along with the total number of degrees of freedom
148			to get the final statistics for the goodness of fit.
149
150			The fit statistics information is updated in the dict object provided and
151			a logging message is passed.
152
153			Parameter
154			---------
155			datasets: `gammapy.datasets.Datasets`
156			List of Datasets object, which can contain 3D and/or 1D datasets
157			instrument_spectral_info: dict
158			Dict of information for storing relevant fit stats
159
160			Return
161			------
162			instrument_spectral_info: dict
163			Filled Dict of information with relevant fit statistics
164			stat_message: str
165			String for logging the fit statistics
166			"""
167			stat_best_fit, stat_max_fit = get_ts_target(datasets)
168
169			instrument_spectral_info["max_fit_stat"] = stat_max_fit
170			instrument_spectral_info["best_fit_stat"] = stat_best_fit
171			ndof = instrument_spectral_info["DoF"]
172			stat_diff_gof = stat_best_fit - stat_max_fit
173
174			fit_chi2_sig, fit_pval = get_chi2_sig_pval(stat_diff_gof, ndof)
175
176			instrument_spectral_info["fit_chi2_sig"] = fit_chi2_sig
177			instrument_spectral_info["fit_pval"] = fit_pval
178
179			stat_message = "The Chi2/dof value of the goodness of Fit is "
180			stat_message += f"{stat_diff_gof:.2f}/{ndof}\nand the p-value is {fit_pval:.3e} "
181			stat_message += f"and in Significance {fit_chi2_sig:.2f} sigmas"
182			stat_message += f"\nwith best fit TS (Observed) as {stat_best_fit:.3f} "
183			stat_message += f"and max fit TS (Expected) as {stat_max_fit:.3f}"
184
185			return instrument_spectral_info, stat_message
186
187
188			def get_ts_target(datasets):
189			"""
190			From a given list of DL4 datasets, with assumed associated models, estimate
191			the total test statistic values, in the given target source region/pixel,
192			for the (observed) best fit of the model to the data, and the (expected)
193			perfect fit of model and data (model = data).
194
195			For consistency in the evaluation of the statistic values, we will use the
196			basic Fit Statistic functions in Gammapy for Poisson Data:
197
198			* `cash <https://docs.gammapy.org/1.1/api/gammapy.stats.cash.html>`_
199
200			* `wstat <https://docs.gammapy.org/1.1/api/gammapy.stats.wstat.html>`_
201
202			For the different type of Statistics used in Gammapy for 3D/1D datasets,
203			and for our use case of getting the best fit and perfect fit, we will pass
204			the appropriate values, by adapting to the following methods,
205
206			* Best Fit (Observed):
207
208			* `Cash stat_array <https://docs.gammapy.org/1.1/api/gammapy.datasets.MapDataset.html#gammapy.datasets.MapDataset.stat_array # noqa>`_
209
210			* `Wstat stat_array <https://docs.gammapy.org/1.1/api/gammapy.datasets.MapDatasetOnOff.html#gammapy.datasets.MapDatasetOnOff.stat_array # noqa>`_
211
212			* Perfect Fit (Expected):
213
214			* `Cash stat_max <https://docs.gammapy.org/1.1/api/gammapy.stats.CashCountsStatistic.html#gammapy.stats.CashCountsStatistic.stat_max # noqa>`_
215
216			* `Wstat stat_max <https://docs.gammapy.org/1.1/api/gammapy.stats.WStatCountsStatistic.html#gammapy.stats.WStatCountsStatistic.stat_max # noqa>`_
217
218			Parameter
219			---------
220			datasets: `gammapy.datasets.Datasets`
221			List of Datasets object, which can contain 3D and/or 1D datasets
222
223			Return
224			------
225			stat_best_fit: float
226			Total sum of test statistic of the best fit of model to data, summed
227			over all energy bins.
228			stat_max_fit: float
229			Test statistic difference of the perfect fit of model to data summed
230			over all energy bins.
231			""" # noqa
232			stat_best_fit = 0
233			stat_max_fit = 0
234
235			for data in datasets:
236			if data.stat_type != "chi2":
237			# Assuming that the Counts Map is created with the target source as its center
238			region = data.counts.geom.center_skydir
239
240			if data.stat_type == "cash":
241			counts_on = (data.counts.copy() * data.mask).get_spectrum(region).data
242			mu_on = (data.npred() * data.mask).get_spectrum(region).data
243
244			stat_best_fit += np.nansum(cash(n_on=counts_on, mu_on=mu_on).ravel())
245			stat_max_fit += np.nansum(cash(n_on=counts_on, mu_on=counts_on).ravel())
246
247			elif data.stat_type == "wstat":
248			counts_on = (data.counts.copy() * data.mask).get_spectrum(region).data
249			counts_off = np.nan_to_num((data.counts_off * data.mask).get_spectrum(region)).data
250
251			# alpha is evaluated by acceptance ratios, and
252			# Background is evaluated with given alpha and counts_off,
253			# but for alpha to be of the same shape (in the target region),
254			# it will be reevaluated
255			bkg = np.nan_to_num((data.background * data.mask).get_spectrum(region))
256
257			with np.errstate(invalid="ignore", divide="ignore"):
258			alpha = bkg / counts_off
259			mu_signal = np.nan_to_num((data.npred_signal() * data.mask).get_spectrum(region)).data
260			max_pred = counts_on - bkg
261
262			stat_best_fit += np.nansum(wstat(n_on=counts_on, n_off=counts_off, alpha=alpha, mu_sig=mu_signal))
263			stat_max_fit += np.nansum(wstat(n_on=counts_on, n_off=counts_off, alpha=alpha, mu_sig=max_pred))
264			else:
265			# For FluxxPointsDataset
266			stat_best_fit += np.nansum(data.stat_array())
267			stat_max_fit += len(data.data.dnde.data)
268
269			return stat_best_fit, stat_max_fit
270
271
272			def pivot_energy(spectral_model):
273			"""
274			Using function of SpectralModel object in Gammapy 1.2.dev build. Will be removed
275			with the new Gammapy release.
276
277			Pivot or decorrelation energy, for a given spectral model calculated numerically.
278
279			It is defined as the energy at which the correlation between the spectral parameters is minimized.
280
281			Returns
282			-------
283			pivot energy : `~astropy.units.Quantity`
284			The energy at which the statistical error in the computed flux is smallest.
285			If no minimum is found, NaN will be returned.
286			"""
287			x_unit = spectral_model.reference.unit
288
289			def min_func(x):
290			"""Function to minimise."""
291			x = np.exp(x)
292			dnde, dnde_error = spectral_model.evaluate_error(x * x_unit)
293			return dnde_error / dnde
294
295			bounds = [np.log(spectral_model.reference.value) - 3, np.log(spectral_model.reference.value) + 3]
296
297			std = np.std(min_func(x=np.linspace(bounds[0], bounds[1], 100)))
298			if std < 1e-5:
299			print("The relative error on the flux does not depend on energy. No pivot energy found.")
300			return np.nan * x_unit
301
302			minimizer = minimize_scalar(min_func, bounds=bounds)
303
304			if not minimizer.success:
305			print("No minima found in the relative error on the flux. Pivot energy computation failed.")
306			return np.nan * x_unit
307			else:
308			return np.exp(minimizer.x) * x_unit
309
310
311			def fetch_pivot_energy(analysis):
312			"""
313			Using an 'AsgardpyAnalysis' object to get the pivot energy for a given dataset
314			and fit model, using the pivot_energy function.
315
316			In Gammapy v1.2, use instead
317			'analysis.fit_result.models[0].spectral_model.model1.pivot_energy' to get this value.
318
319			Returns
320			-------
321			pivot energy : `~astropy.units.Quantity`
322			The energy at which the statistical error in the computed flux is smallest.
323			If no minimum is found, NaN will be returned.
324			"""
325			# Check if DL4 datasets are created, and if not, only run steps till Fit
326			if len(analysis.datasets) == 0:
327			steps = [step for step in analysis.config.general.steps if step != "flux-points"]
328
329			analysis.run(steps)
330			else:
331			analysis.run(["fit"])
332
333			# Assuming EBL model is present
334			if isinstance(analysis.datasets[0].models[0].spectral_model, CompoundSpectralModel):
335			temp_model = analysis.datasets[0].models[0].spectral_model.model1
336			else:
337			temp_model = analysis.datasets[0].models[0].spectral_model
338
339			# Fetching the covariance matrix for the given dataset and optimized fit model
340			cov_matrix = analysis.fit.covariance(
341			datasets=analysis.datasets, optimize_result=analysis.fit_result.optimize_result
342			).matrix
343
344			temp_model.covariance = cov_matrix[: len(temp_model.parameters), : len(temp_model.parameters)]
345
346			return pivot_energy(temp_model)
347

chaimain / asgardpy

Pull Request — main (#160)

asgardpy.stats.stats.fetch_pivot_energy() A

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