Up: Plot Commands
make a plot
Make one or more plots to the current plot device (see cpd or setplot device).
||<plot type>[<plot type>] [<plot type>] ...
<plot type> is a keyword describing the various plots allowed. Up
to six plot panes can be put on a single page by combining multiple
<plot type> options. For example:
XSPEC12> plot data resid ratio model
will produce a 4-pane plot. However contour plots may not be combined
with other plots in this manner. When a certain plot type takes additional
arguments (eg. chain, model), simply list them in order
prior to specifying the next plot type:
XSPEC12> plot chain 3 4 data ufspec
In multi-pane plots, XSPEC will determine if two consecutive plot types may
share a common X-axis (e.g. plot data delchi, or plot counts
ratio). If so, the first pane will be stacked directly on top of the second.
(Note that the small subset of multi-pane plots that were allowed in earlier
versions of XSPEC all belonged in this category.)
For changing plot units, see setplot energy and setplot wave.
Also see iplot for performing interactive plots.
Plot only the background spectra (with folded model, if defined). To plot
both the data and background spectra, use plot data with the
setplot background option.
Plot a Monte Carlo Markov chain.
plot chain [thin <n>] <par1>[<par2>]
Chains must be currently loaded (see chain command), and <par1>
and <par2> are parameter identifiers of the form [<model name>:]<n>
where <n> is an integer, specifying the parameter columns in the chain
file to serve as the X and Y axes respectively. To select the fit-statistic
column, enter '0' for the <par> value. If <par2> is omitted,
<par1> is simply plotted against row number.
Use the thin <n> option to display only 1 out of every
<n> chain points. Example:
# plot one in five chain points,
# using parameters 1 and 4 for (X,Y)
plot chain thin 5 1 4
The thin value will be retained for future chain plots until it
is reset. Enter thin 1 to remove thinning.
Plot contributions to chisq. The contribution is plotted +ve or -ve
depending on whether the residual is +ve or -ve.
Plot the results of the last steppar run. If this was over one
parameter then a plot of statistic versus parameter value is produced while
a steppar over two parameters results in a fit-statistic contour plot.
plot contour [<min fit stat>[<# levels>[<levels>]]]
where <min fit stat> is the minimum fit statistic relative to which
the delta fit statistic is calculated, <# levels> is the number of
contour levels to use and <levels> := <level1> ... <levelN>
are the contour levels in the delta fit statistic. contour will
plot the fit statistic grid calculated by the last steppar command
(which should have gridded on two parameters). A small plus sign '+' will
be drawn on the plot at the parameter values corresponding to the
minimum found by the most recent fit.
The fit statistic confidence contours are often drawn based on a relatively
small grid (i.e., 5x5). To understand fully what these plots are telling you,
it is useful to know a couple of points concerning how the software chooses
the location of the contour lines. The contour plot is drawn based only on
the information contained in the sample grid. For example, if the minimum
fit statistic occurs when parameter 1 equals 2.25 and you use steppar 1 1.0 5.0 4,
then the grid values closest to the minimum are 2.0 and 3.0. This could
mean that there are no grid points where delta-fit statistic is less than
your lowest level (which defaults to 1.0). As a result, the lowest contour
will not be drawn. This effect can be minimized by always selecting a
steppar range that causes XSPEC to step very close to the true minima.
For the above example, using steppar 1 1.25 5.25 4, would have been
a better selection. The location of a contour line between grid points is
designated using a linear interpolation. Since the fit statistic surface is
often quadratic, a linear interpolation will result in the lines being drawn
inside the true location of the contour. The combination of this and the
previous effect sometimes will result in the minimum found by the fit
command lying outside the region enclosed by the lowest contour level.
A grey-scale image of the data being contoured is also plotted. This
can be removed by using the PLT command image off.
XSPEC12> steppar 2 0.5 1. 4 3 1. 2. 4
// create a grid for parameters 2 and 3
XSPEC12> plot contour
// Plot out a grid with three contours with
// delta fit statistic of 2.3, 4.61 and 9.21
XSPEC12> plot cont,,4,1.,2.3,4.61,9.21
// same as above, but with a delta fit statistic = 1 contour.
Plot the data (with the folded model, if defined) with the y-axis being
numbers of counts in each bin.
Plot the data (with the folded model, if defined).
Plot the residuals in terms of sigmas with error bars of size one. In the
case of the cstat and related statistics this plots (data-model)/error
where error is calculated as the square root of the model predicted number
of counts. Note that in this case this is not the same as
contributions to the statistic.
Plot a histogram of the relative contributions of plasma at different
temperatures for multi-temperature models. This is not very clever at
the moment and only plots the last model calculated.
Plot the total response efficiency versus incident photon energy.
Plot the probability density of the most recently run eqwidth
calculation with error estimate.
Plot a histogram of the statistics calculated for each simulation of the
most recent goodness command run.
Integrated counts and folded model. The integrated counts are normalized to unity.
Plot the insensitivity of the current spectrum to changes in the incident spectra (experimental).
Plot the integrated probability distribution from the results of the most recently
run margin command (must be a 1-D or 2-D distribution). The
integrated probability is calculated by summing bins in decreasing
order of probability. A grey-scale image of the data being contoured
is also plotted. This can be removed by using the PLT command image off.
Plot the data (with the folded model, if defined) with a logarithmic y-axis
indicating the count spectrum
Plot the data (with the folded model, if defined) with a logarithmic y-axis.
Plot the probability distribution from the results of the most recently
run margin command (must be a 1-D or 2-D distribution). A
grey-scale image of the data being contoured is also plotted. This
can be removed by using the PLT command image off.
- model, emodel, eemodel
Plot the current incident model spectrum (Note: This is NOT the same as an
unfolded spectrum.) The contributions of the various additive components are
also plotted. If using a named model, the model name should be given as an
additional argument. emodel plots or, if plotting wavelength,
. eemodel plots , or if plotting
. The (or ) used in the
multiplicative factor is taken to be the geometric mean of the lower and
upper energies of the plot bin.
Plot the data divided by the folded model.
Plot the data minus the folded model.
Plot the sensitivity of the current spectrum to changes in the incident spectra (experimental).
- A pretty plot of the data and residuals against both channels and energy.
- ufspec, eufspec, eeufspec
Plot the unfolded spectrum and the model. The contributions to the model
of the various additive components also are plotted. WARNING ! This plot
is not model-independent and your unfolded model points will move if the
model is changed. The data points plotted are calculated by
D*(unfolded model)/(folded model), where D is the observed data,
(unfolded model) is the theoretical model integrated over the plot bin,
and (folded model) is the model times the response as seen in the standard
plot data. eufspec plots the unfolded spectrum and model
in , or if plotting wavelength,
plots the unfolded spectrum and model in , or if plotting wavelength,
. The E (or ) used in the multiplicative factor
is taken to be the geometric mean of the lower and upper energies of the plot
Up: Plot Commands