plot_energy_flux¶

sherpa.astro.ui.plot_energy_flux(lo=None, hi=None, id=None, num=7500, bins=75, correlated=False, numcores=None, bkg_id=None, scales=None, model=None, otherids=(), recalc=True, overplot=False, clearwindow=True, **kwargs)

Display the energy flux distribution.

For each iteration, draw the parameter values of the model from a normal distribution, evaluate the model, and sum the model over the given range (the flux). Plot up the distribution of this flux. The units for the flux are as returned by calc_energy_flux. The sample_energy_flux and get_energy_flux_hist functions return the data used to create this plot.

Changed in version 4.12.2: The scales parameter is no longer ignored when set and the model and otherids parameters have been added.

Parameters
• lo (number, optional) – The lower limit to use when summing up the signal. If not given then the lower value of the data grid is used.

• hi (optional) – The upper limit to use when summing up the signal. If not given then the upper value of the data grid is used.

• id (int or string, optional) – The identifier of the data set to use. If None, the default value, then all datasets with associated models are used to calculate the errors and the model evaluation is done using the default dataset.

• num (int, optional) – The number of samples to create. The default is 7500.

• bins (int, optional) – The number of bins to use for the histogram.

• correlated (bool, optional) – If True (the default is False) then scales is the full covariance matrix, otherwise it is just a 1D array containing the variances of the parameters (the diagonal elements of the covariance matrix).

• numcores (optional) – The number of CPU cores to use. The default is to use all the cores on the machine.

• bkg_id (int or string, optional) – The identifier of the background component to use. This should only be set when the line to be measured is in the background model.

• scales (array, optional) – The scales used to define the normal distributions for the parameters. The size and shape of the array depends on the number of free parameters in the fit (n) and the value of the correlated parameter. When the parameter is True, scales must be given the covariance matrix for the free parameters (a n by n matrix that matches the parameter ordering used by Sherpa). For un-correlated parameters the covariance matrix can be used, or a one-dimensional array of n elements can be used, giving the width (specified as the sigma value of a normal distribution) for each parameter (e.g. the square root of the diagonal elements of the covariance matrix). If the scales parameter is not given then the covariance matrix is evaluated for the current model and best-fit parameters.

• model (model, optional) – The model to integrate. If left as None then the source model for the dataset will be used. This can be used to calculate the unabsorbed flux, as shown in the examples. The model must be part of the source expression.

• otherids (sequence of integer and string ids, optional) – The list of other datasets that should be included when calculating the errors to draw values from.

• recalc (bool, optional) – If True, the default, then re-calculate the values rather than use the values from the last time the function was run.

• overplot (bool, optional) – If True then add the data to an exsiting plot, otherwise create a new plot. The default is False.

• clearwindow (bool, optional) – Should the existing plot area be cleared before creating this new plot (e.g. for multi-panel plots)?

calc_photon_flux()

Integrate the unconvolved source model over a pass band.

calc_energy_flux()

Integrate the unconvolved source model over a pass band.

covar()

Estimate the confidence intervals using the confidence method.

get_energy_flux_hist()

Return the data displayed by plot_energy_flux.

get_photon_flux_hist()

Return the data displayed by plot_photon_flux.

plot_cdf()

Plot the cumulative density function of an array.

plot_pdf()

Plot the probability density function of an array.

plot_photon_flux()

Display the photon flux distribution.

plot_trace()

Create a trace plot of row number versus value.

sample_energy_flux()

Return the energy flux distribution of a model.

sample_flux()

Return the flux distribution of a model.

sample_photon_flux()

Return the photon flux distribution of a model.

Examples

Plot the energy flux distribution for the range 0.5 to 7 for the default data set:

>>> plot_energy_flux(0.5, 7, num=1000)


Overplot the 0.5 to 2 energy flux distribution from the “core” data set on top of the values from the “jet” data set:

>>> plot_energy_flux(0.5, 2, id="jet", num=1000)
>>> plot_energy_flux(0.5, 2, id="core", num=1000, overplot=True)


Overplot the flux distribution for just the pl component (which must be part of the source expression) on top of the full model. If the full model was xsphabs.gal * powlaw1d.pl then this will compare the unabsorbed to absorbed flux distributions:

>>> plot_energy_flux(0.5, 2, num=1000, bins=20)
>>> plot_energy_flux(0.5, 2, model=pl, num=1000, bins=20)


If you have multiple datasets loaded, each with a model, then all datasets will be used to calculate the errors when the id parameter is not set. A single dataset can be used by specifying a dataset (in this example the overplot is just with dataset 1):

>>> mdl = xsphabs.gal * xsapec.src
>>> set_source(1, mdl)
>>> set_source(2, mdl)
...
>>> plot_energy_flux(0.5, 2, model=src num=1000, bins=20)
>>> plot_energy_flux(0.5, 2, model=src num=1000, bins=20,
...                  id=1, overplot=True)


If you have multiple datasets then you can use the otherids argument to specify exactly what set of data is used:

>>> plot_energy_flux(0.5, 2, model=src num=1000, bins=20,
...                  id=1, otherids=(2, 3, 4))