MapDataset#

class gammapy.datasets.MapDataset(models=None, counts=None, exposure=None, background=None, psf=None, edisp=None, mask_safe=None, mask_fit=None, gti=None, meta_table=None, name=None, meta=None)[source]#

Bases: gammapy.datasets.core.Dataset

Main map dataset for likelihood fitting.

It bundles together binned counts, background, IRFs in the form of Map. A safe mask and a fit mask can be added to exclude bins during the analysis. If models are assigned to it, it can compute predicted counts in each bin of the counts Map and compute the associated statistic function, here the Cash statistic (see cash).

For more information see Datasets (DL4).

Parameters
modelsModels

Source sky models.

countsWcsNDMap or HDULocation

Counts cube.

exposureWcsNDMap or HDULocation

Exposure cube.

backgroundWcsNDMap or HDULocation

Background cube.

mask_fitWcsNDMap or HDULocation

Mask to apply to the likelihood for fitting.

psfPSFMap or HDULocation

PSF kernel.

edispEDispMap or HDULocation

Energy dispersion kernel

mask_safeWcsNDMap or HDULocation

Mask defining the safe data range.

gtiGTI

GTI of the observation or union of GTI if it is a stacked observation.

meta_tableTable

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

metaMapDatasetMetaData

Associated meta data container

See also

MapDatasetOnOff, SpectrumDataset, FluxPointsDataset.

Notes

If an HDULocation is passed the map is loaded lazily. This means the map data is only loaded in memory as the corresponding data attribute on the MapDataset is accessed. If it was accessed once it is cached for the next time.

Examples

>>> from gammapy.datasets import MapDataset
>>> filename = "$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz"
>>> dataset = MapDataset.read(filename, name="cta-dataset")
>>> print(dataset)
MapDataset
----------

  Name                            : cta-dataset

  Total counts                    : 104317
  Total background counts         : 91507.70
  Total excess counts             : 12809.30

  Predicted counts                : 91507.69
  Predicted background counts     : 91507.70
  Predicted excess counts         : nan

  Exposure min                    : 6.28e+07 m2 s
  Exposure max                    : 1.90e+10 m2 s

  Number of total bins            : 768000
  Number of fit bins              : 691680

  Fit statistic type              : cash
  Fit statistic value (-2 log(L)) : nan

  Number of models                : 0
  Number of parameters            : 0
  Number of free parameters       : 0

Attributes Summary

background

A lazy FITS data descriptor.

background_model

counts

A lazy FITS data descriptor.

data_shape

Shape of the counts or background data (tuple).

edisp

A lazy FITS data descriptor.

energy_range

Energy range maps defined by the mask_safe and mask_fit.

energy_range_fit

Energy range maps defined by the mask_fit only.

energy_range_safe

Energy range maps defined by the mask_safe only.

energy_range_total

Largest energy range among all pixels, defined by mask_safe and mask_fit.

evaluators

Model evaluators.

excess

Observed excess: counts-background.

exposure

A lazy FITS data descriptor.

geoms

Map geometries.

gti

mask

Combined fit and safe mask.

mask_fit

A lazy FITS data descriptor.

mask_fit_image

Reduced fit mask.

mask_image

Reduced mask.

mask_safe

A lazy FITS data descriptor.

mask_safe_edisp

Safe mask for edisp maps.

mask_safe_image

Reduced safe mask.

mask_safe_psf

Safe mask for PSF maps.

meta

meta_table

models

Models set on the dataset (Models).

name

psf

A lazy FITS data descriptor.

stat_type

tag

Methods Summary

copy([name])

A deep copy.

create(geom[, energy_axis_true, migra_axis, ...])

Create a MapDataset object with zero filled maps.

cutout(position, width[, mode, name])

Cutout map dataset.

downsample(factor[, axis_name, name])

Downsample map dataset.

fake([random_state])

Simulate fake counts for the current model and reduced IRFs.

from_dict(data[, lazy, cache])

Create from dicts and models list generated from YAML serialization.

from_geoms(geom[, geom_exposure, geom_psf, ...])

Create a MapDataset object with zero filled maps according to the specified geometries.

from_hdulist(hdulist[, name, lazy, format])

Create map dataset from list of HDUs.

info_dict([in_safe_data_range])

Info dict with summary statistics, summed over energy.

npred()

Total predicted source and background counts.

npred_background()

Predicted background counts.

npred_signal([model_names, stack])

Model predicted signal counts.

pad(pad_width[, mode, name])

Pad the spatial dimensions of the dataset.

peek([figsize])

Quick-look summary plots.

plot_residuals([ax_spatial, ax_spectral, ...])

Plot spatial and spectral residuals in two panels.

plot_residuals_spatial([ax, method, ...])

Plot spatial residuals.

plot_residuals_spectral([ax, method, region])

Plot spectral residuals.

read(filename[, name, lazy, cache, format, ...])

Read a dataset from file.

resample_energy_axis(energy_axis[, name])

Resample MapDataset over new reco energy axis.

reset_data_cache()

Reset data cache to free memory space.

residuals([method])

Compute residuals map.

slice_by_energy([energy_min, energy_max, name])

Select and slice datasets in energy range.

slice_by_idx(slices[, name])

Slice sub dataset.

stack(other[, nan_to_num])

Stack another dataset in place.

stat_array()

Statistic function value per bin given the current model parameters.

stat_sum()

Total statistic function value given the current model parameters and priors.

to_dict()

Convert to dict for YAML serialization.

to_hdulist()

Convert map dataset to list of HDUs.

to_image([name])

Create images by summing over the reconstructed energy axis.

to_masked([name, nan_to_num])

Return masked dataset.

to_region_map_dataset(region[, name])

Integrate the map dataset in a given region.

to_spectrum_dataset(on_region[, ...])

Return a ~gammapy.datasets.SpectrumDataset from on_region.

write(filename[, overwrite, checksum])

Write Dataset to file.

Attributes Documentation

background#

A lazy FITS data descriptor.

Parameters
cachebool

Whether to cache the data.

background_model#
counts#

A lazy FITS data descriptor.

Parameters
cachebool

Whether to cache the data.

data_shape#

Shape of the counts or background data (tuple).

edisp#

A lazy FITS data descriptor.

Parameters
cachebool

Whether to cache the data.

energy_range#

Energy range maps defined by the mask_safe and mask_fit.

energy_range_fit#

Energy range maps defined by the mask_fit only.

energy_range_safe#

Energy range maps defined by the mask_safe only.

energy_range_total#

Largest energy range among all pixels, defined by mask_safe and mask_fit.

evaluators#

Model evaluators.

excess#

Observed excess: counts-background.

exposure#

A lazy FITS data descriptor.

Parameters
cachebool

Whether to cache the data.

geoms#

Map geometries.

Returns
geomsdict

Dictionary of map geometries involved in the dataset.

gti = None#
mask#

Combined fit and safe mask.

mask_fit#

A lazy FITS data descriptor.

Parameters
cachebool

Whether to cache the data.

mask_fit_image#

Reduced fit mask.

mask_image#

Reduced mask.

mask_safe#

A lazy FITS data descriptor.

Parameters
cachebool

Whether to cache the data.

mask_safe_edisp#

Safe mask for edisp maps.

mask_safe_image#

Reduced safe mask.

mask_safe_psf#

Safe mask for PSF maps.

meta#
meta_table = None#
models#

Models set on the dataset (Models).

name#
psf#

A lazy FITS data descriptor.

Parameters
cachebool

Whether to cache the data.

stat_type = 'cash'#
tag = 'MapDataset'#

Methods Documentation

copy(name=None)#

A deep copy.

Parameters
namestr, optional

Name of the copied dataset. Default is None.

Returns
datasetDataset

Copied datasets.

classmethod create(geom, energy_axis_true=None, migra_axis=None, rad_axis=None, binsz_irf=<Quantity 0.2 deg>, reference_time='2000-01-01', name=None, meta_table=None, reco_psf=False, **kwargs)[source]#

Create a MapDataset object with zero filled maps.

Parameters
geomWcsGeom

Reference target geometry in reco energy, used for counts and background maps.

energy_axis_trueMapAxis, optional

True energy axis used for IRF maps. Default is None.

migra_axisMapAxis, optional

If set, this provides the migration axis for the energy dispersion map. If not set, an EDispKernelMap is produced instead. Default is None.

rad_axisMapAxis, optional

Rad axis for the PSF map. Default is None.

binsz_irffloat

IRF Map pixel size in degrees. Default is BINSZ_IRF_DEFAULT.

reference_timeTime

The reference time to use in GTI definition. Default is “2000-01-01”.

namestr, optional

Name of the returned dataset. Default is None.

meta_tableTable, optional

Table listing information on observations used to create the dataset. One line per observation for stacked datasets. Default is None.

reco_psfbool

Use reconstructed energy for the PSF geometry. Default is False.

Returns
empty_mapsMapDataset

A MapDataset containing zero filled maps.

Examples

>>> from gammapy.datasets import MapDataset
>>> from gammapy.maps import WcsGeom, MapAxis
>>> energy_axis = MapAxis.from_energy_bounds(1.0, 10.0, 4, unit="TeV")
>>> energy_axis_true = MapAxis.from_energy_bounds(
...            0.5, 20, 10, unit="TeV", name="energy_true"
...        )
>>> geom = WcsGeom.create(
...            skydir=(83.633, 22.014),
...            binsz=0.02, width=(2, 2),
...            frame="icrs",
...            proj="CAR",
...            axes=[energy_axis]
...        )
>>> empty = MapDataset.create(geom=geom, energy_axis_true=energy_axis_true, name="empty")
cutout(position, width, mode='trim', name=None)[source]#

Cutout map dataset.

Parameters
positionSkyCoord

Center position of the cutout region.

widthtuple of Angle

Angular sizes of the region in (lon, lat) in that specific order. If only one value is passed, a square region is extracted.

mode{‘trim’, ‘partial’, ‘strict’}

Mode option for Cutout2D, for details see Cutout2D. Default is “trim”.

namestr, optional

Name of the new dataset. Default is None.

Returns
cutoutMapDataset

Cutout map dataset.

downsample(factor, axis_name=None, name=None)[source]#

Downsample map dataset.

The PSFMap and EDispKernelMap are not downsampled, except if a corresponding axis is given.

Parameters
factorint

Downsampling factor.

axis_namestr, optional

Which non-spatial axis to downsample. By default only spatial axes are downsampled. Default is None.

namestr, optional

Name of the downsampled dataset. Default is None.

Returns
datasetMapDataset or SpectrumDataset

Downsampled map dataset.

fake(random_state='random-seed')[source]#

Simulate fake counts for the current model and reduced IRFs.

This method overwrites the counts defined on the dataset object.

Parameters
random_state{int, ‘random-seed’, ‘global-rng’, RandomState}

Defines random number generator initialisation. Passed to get_random_state. Default is “random-seed”.

classmethod from_dict(data, lazy=False, cache=True)[source]#

Create from dicts and models list generated from YAML serialization.

classmethod from_geoms(geom, geom_exposure=None, geom_psf=None, geom_edisp=None, reference_time='2000-01-01', name=None, **kwargs)[source]#

Create a MapDataset object with zero filled maps according to the specified geometries.

Parameters
geomGeom

Geometry for the counts and background maps.

geom_exposureGeom

Geometry for the exposure map. Default is None.

geom_psfGeom

Geometry for the PSF map. Default is None.

geom_edispGeom

Geometry for the energy dispersion kernel map. If geom_edisp has a migra axis, this will create an EDispMap instead. Default is None.

reference_timeTime

The reference time to use in GTI definition. Default is “2000-01-01”.

namestr

Name of the returned dataset. Default is None.

kwargsdict, optional

Keyword arguments to be passed.

Returns
datasetMapDataset or SpectrumDataset

A dataset containing zero filled maps.

classmethod from_hdulist(hdulist, name=None, lazy=False, format='gadf')[source]#

Create map dataset from list of HDUs.

Parameters
hdulistHDUList

List of HDUs.

namestr, optional

Name of the new dataset. Default is None.

lazybool

Whether to lazy load data into memory. Default is False.

format{“gadf”}

Format the hdulist is given in. Default is “gadf”.

Returns
datasetMapDataset

Map dataset.

info_dict(in_safe_data_range=True)[source]#

Info dict with summary statistics, summed over energy.

Parameters
in_safe_data_rangebool

Whether to sum only in the safe energy range. Default is True.

Returns
info_dictdict

Dictionary with summary info.

npred()[source]#

Total predicted source and background counts.

Returns
npredMap

Total predicted counts.

npred_background()[source]#

Predicted background counts.

The predicted background counts depend on the parameters of the FoVBackgroundModel defined in the dataset.

Returns
npred_backgroundMap

Predicted counts from the background.

npred_signal(model_names=None, stack=True)[source]#

Model predicted signal counts.

If a list of model name is passed, predicted counts from these components are returned. If stack is set to True, a map of the sum of all the predicted counts is returned. If stack is set to False, a map with an additional axis representing the models is returned.

Parameters
model_nameslist of str

List of name of SkyModel for which to compute the npred. If none, all the SkyModel predicted counts are computed.

stackbool

Whether to stack the npred maps upon each other.

Returns
npred_siggammapy.maps.Map

Map of the predicted signal counts.

pad(pad_width, mode='constant', name=None)[source]#

Pad the spatial dimensions of the dataset.

The padding only applies to counts, masks, background and exposure.

Counts, background and masks are padded with zeros, exposure is padded with edge value.

Parameters
pad_width{sequence, array_like, int}

Number of pixels padded to the edges of each axis.

modestr

Pad mode. Default is “constant”.

namestr, optional

Name of the padded dataset. Default is None.

Returns
datasetMapDataset

Padded map dataset.

peek(figsize=(12, 8))[source]#

Quick-look summary plots.

Parameters
figsizetuple

Size of the figure. Default is (12, 10).

plot_residuals(ax_spatial=None, ax_spectral=None, kwargs_spatial=None, kwargs_spectral=None)[source]#

Plot spatial and spectral residuals in two panels.

Calls plot_residuals_spatial and plot_residuals_spectral. The spectral residuals are extracted from the provided region, and the normalization used for its computation can be controlled using the method parameter. The region outline is overlaid on the residuals map. If no region is passed, the residuals are computed for the entire map.

Parameters
ax_spatialWCSAxes, optional

Axes to plot spatial residuals on. Default is None.

ax_spectralAxes, optional

Axes to plot spectral residuals on. Default is None.

kwargs_spatialdict, optional

Keyword arguments passed to plot_residuals_spatial. Default is None.

kwargs_spectraldict, optional

Keyword arguments passed to plot_residuals_spectral. The region should be passed as a dictionary key. Default is None.

Returns
ax_spatial, ax_spectralWCSAxes, Axes

Spatial and spectral residuals plots.

Examples

>>> from regions import CircleSkyRegion
>>> from astropy.coordinates import SkyCoord
>>> import astropy.units as u
>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> reg = CircleSkyRegion(SkyCoord(0,0, unit="deg", frame="galactic"), radius=1.0 * u.deg)
>>> kwargs_spatial = {"cmap": "RdBu_r", "vmin":-5, "vmax":5, "add_cbar": True}
>>> kwargs_spectral = {"region":reg, "markerfacecolor": "blue", "markersize": 8, "marker": "s"}
>>> dataset.plot_residuals(kwargs_spatial=kwargs_spatial, kwargs_spectral=kwargs_spectral) 
plot_residuals_spatial(ax=None, method='diff', smooth_kernel='gauss', smooth_radius='0.1 deg', **kwargs)[source]#

Plot spatial residuals.

The normalization used for the residuals computation can be controlled using the method parameter.

Parameters
axWCSAxes, optional

Axes to plot on. Default is None.

method{“diff”, “diff/model”, “diff/sqrt(model)”}

Normalization used to compute the residuals, see MapDataset.residuals. Default is “diff”.

smooth_kernel{“gauss”, “box”}

Kernel shape. Default is “gauss”.

smooth_radius: `~astropy.units.Quantity`, str or float

Smoothing width given as quantity or float. If a float is given, it is interpreted as smoothing width in pixels. Default is “0.1 deg”.

**kwargsdict, optional

Keyword arguments passed to imshow.

Returns
axWCSAxes

WCSAxes object.

Examples

>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> kwargs = {"cmap": "RdBu_r", "vmin":-5, "vmax":5, "add_cbar": True}
>>> dataset.plot_residuals_spatial(method="diff/sqrt(model)", **kwargs) 
plot_residuals_spectral(ax=None, method='diff', region=None, **kwargs)[source]#

Plot spectral residuals.

The residuals are extracted from the provided region, and the normalization used for its computation can be controlled using the method parameter.

The error bars are computed using the uncertainty on the excess with a symmetric assumption.

Parameters
axAxes, optional

Axes to plot on. Default is None.

method{“diff”, “diff/sqrt(model)”}

Normalization used to compute the residuals, see SpectrumDataset.residuals. Default is “diff”.

regionSkyRegion (required)

Target sky region. Default is None.

**kwargsdict, optional

Keyword arguments passed to errorbar.

Returns
axAxes

Axes object.

Examples

>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> kwargs = {"markerfacecolor": "blue", "markersize":8, "marker":'s'}
>>> dataset.plot_residuals_spectral(method="diff/sqrt(model)", **kwargs) 
classmethod read(filename, name=None, lazy=False, cache=True, format='gadf', checksum=False)[source]#

Read a dataset from file.

Parameters
filenamestr

Filename to read from.

namestr, optional

Name of the new dataset. Default is None.

lazybool

Whether to lazy load data into memory. Default is False.

cachebool

Whether to cache the data after loading. Default is True.

format{“gadf”}

Format of the dataset file. Default is “gadf”.

checksumbool

If True checks both DATASUM and CHECKSUM cards in the file headers. Default is False.

Returns
datasetMapDataset

Map dataset.

resample_energy_axis(energy_axis, name=None)[source]#

Resample MapDataset over new reco energy axis.

Counts are summed taking into account safe mask.

Parameters
energy_axisMapAxis

New reconstructed energy axis.

namestr, optional

Name of the new dataset. Default is None.

Returns
datasetMapDataset or SpectrumDataset

Resampled dataset.

reset_data_cache()[source]#

Reset data cache to free memory space.

residuals(method='diff', **kwargs)[source]#

Compute residuals map.

Parameters
method{“diff”, “diff/model”, “diff/sqrt(model)”}
Method used to compute the residuals. Available options are:
  • “diff” (default): data - model.

  • “diff/model”: (data - model) / model.

  • “diff/sqrt(model)”: (data - model) / sqrt(model).

Default is “diff”.

**kwargsdict, optional

Keyword arguments forwarded to Map.smooth().

Returns
residualsgammapy.maps.Map

Residual map.

slice_by_energy(energy_min=None, energy_max=None, name=None)[source]#

Select and slice datasets in energy range.

Parameters
energy_min, energy_maxQuantity, optional

Energy bounds to compute the flux point for. Default is None.

namestr, optional

Name of the sliced dataset. Default is None.

Returns
datasetMapDataset

Sliced Dataset.

Examples

>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> sliced = dataset.slice_by_energy(energy_min="1 TeV", energy_max="5 TeV")
>>> sliced.data_shape
(3, 240, 320)
slice_by_idx(slices, name=None)[source]#

Slice sub dataset.

The slicing only applies to the maps that define the corresponding axes.

Parameters
slicesdict

Dictionary of axes names and integers or slice object pairs. Contains one element for each non-spatial dimension. For integer indexing the corresponding axes is dropped from the map. Axes not specified in the dict are kept unchanged.

namestr, optional

Name of the sliced dataset. Default is None.

Returns
datasetMapDataset or SpectrumDataset

Sliced dataset.

Examples

>>> from gammapy.datasets import MapDataset
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> slices = {"energy": slice(0, 3)} #to get the first 3 energy slices
>>> sliced = dataset.slice_by_idx(slices)
>>> print(sliced.geoms["geom"])
WcsGeom
        axes       : ['lon', 'lat', 'energy']
        shape      : (320, 240, 3)
        ndim       : 3
        frame      : galactic
        projection : CAR
        center     : 0.0 deg, 0.0 deg
        width      : 8.0 deg x 6.0 deg
        wcs ref    : 0.0 deg, 0.0 deg
stack(other, nan_to_num=True)[source]#

Stack another dataset in place. The original dataset is modified.

Safe mask is applied to compute the stacked counts data. Counts outside each dataset safe mask are lost.

The stacking of 2 datasets is implemented as follows. Here, \(k\) denotes a bin in reconstructed energy and \(j = {1,2}\) is the dataset number.

The mask_safe of each dataset is defined as:

\[\begin{split}\epsilon_{jk} =\left\{\begin{array}{cl} 1, & \mbox{if bin k is inside the thresholds}\\ 0, & \mbox{otherwise} \end{array}\right.\end{split}\]

Then the total counts and model background bkg are computed according to:

\[ \begin{align}\begin{aligned}\overline{\mathrm{n_{on}}}_k = \mathrm{n_{on}}_{1k} \cdot \epsilon_{1k} + \mathrm{n_{on}}_{2k} \cdot \epsilon_{2k}.\\\overline{bkg}_k = bkg_{1k} \cdot \epsilon_{1k} + bkg_{2k} \cdot \epsilon_{2k}.\end{aligned}\end{align} \]

The stacked safe_mask is then:

\[\overline{\epsilon_k} = \epsilon_{1k} OR \epsilon_{2k}.\]
Parameters
otherMapDataset or MapDatasetOnOff

Map dataset to be stacked with this one. If other is an on-off dataset alpha * counts_off is used as a background model.

nan_to_numbool

Non-finite values are replaced by zero if True. Default is True.

stat_array()[source]#

Statistic function value per bin given the current model parameters.

stat_sum()[source]#

Total statistic function value given the current model parameters and priors.

to_dict()#

Convert to dict for YAML serialization.

to_hdulist()[source]#

Convert map dataset to list of HDUs.

Returns
hdulistHDUList

Map dataset list of HDUs.

to_image(name=None)[source]#

Create images by summing over the reconstructed energy axis.

Parameters
namestr, optional

Name of the new dataset. Default is None.

Returns
datasetMapDataset or SpectrumDataset

Dataset integrated over non-spatial axes.

to_masked(name=None, nan_to_num=True)[source]#

Return masked dataset.

Parameters
namestr, optional

Name of the masked dataset. Default is None.

nan_to_numbool

Non-finite values are replaced by zero if True. Default is True.

Returns
datasetMapDataset or SpectrumDataset

Masked dataset.

to_region_map_dataset(region, name=None)[source]#

Integrate the map dataset in a given region.

Counts and background of the dataset are integrated in the given region, taking the safe mask into account. The exposure is averaged in the region again taking the safe mask into account. The PSF and energy dispersion kernel are taken at the center of the region.

Parameters
regionSkyRegion

Region from which to extract the spectrum.

namestr, optional

Name of the new dataset. Default is None.

Returns
datasetMapDataset

The resulting reduced dataset.

to_spectrum_dataset(on_region, containment_correction=False, name=None)[source]#

Return a ~gammapy.datasets.SpectrumDataset from on_region.

Counts and background are summed in the on_region. Exposure is taken from the average exposure.

The energy dispersion kernel is obtained at the on_region center. Only regions with centers are supported.

The model is not exported to the ~gammapy.datasets.SpectrumDataset. It must be set after the dataset extraction.

Parameters
on_regionSkyRegion

The input ON region on which to extract the spectrum.

containment_correctionbool

Apply containment correction for point sources and circular on regions. Default is False.

namestr, optional

Name of the new dataset. Default is None.

Returns
datasetSpectrumDataset

The resulting reduced dataset.

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

Write Dataset to file.

A MapDataset is serialised using the GADF format with a WCS geometry. A SpectrumDataset uses the same format, with a RegionGeom.

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 file. Default is False.