Usage¶

Configuration file¶

First you need to create a configuration file, see data/example_config.toml included in the package or in the repository.

It first defines some global configuration options like output \(N_{side}\), the unit and the channels, then has 2 subsections. They both define subsections with a class attribute that specifies which object should be instantiated; all other arguments are passed into the class constructor.

The pysm_components subsection allows to choose any pre-existing PySM model and later add any custom class, for example one from so_pysm_models.
The other_components section instead includes models that generate a map to be summed after PySM has been executed, for example the noise simulation.

channels supports both simulating the Simons Observatory channels at single frequencies or top-hat bandpasses. If you specify channels named by the telescope and the frequency in GHz "SA_27", the simulations are performed at a single frequency. Instead if you specify one of the bandpasses, for example "LA_MFF1", the simulations are executed with top-hat bandpasses (10 equally spaced points within the band integrated with the Trapezoidal rule). If you specify shortcuts for groups of channels, i.e. "all", "LA" or "SA", top-hat bandpasses are selected.

Simulate other instruments¶

A custom instrument can be defined by providing instrument parameters via a HDF5 file in a specified format, see MapSim for details on the format. Planck channels at single frequencies are embedded in the package, pass instrument_parameters="planck_deltabandpass" to select it. See also planck_deltabandpass.h5 in the mapsims/data folder as an example of the format.

Example of accessing the file:

In [1]: import h5py

In [2]: f = h5py.File("planck_deltabandpass.h5")

In [3]: f.keys()
Out[3]: <KeysViewHDF5 ['030', '044', '070', '100', '143', '217', '353', '545', '857']>

In [4]: f["143"].attrs
Out[4]: <Attributes of HDF5 object at 46913457708992>

In [5]: list(f["143"].attrs)
Out[5]: ['band', 'center_frequency_GHz', 'fwhm_arcmin']

In [6]: f["143"].attrs["center_frequency_GHz"]
Out[6]: 142.876

mapsims_run¶

mapsims_run is a script included in the package, it can be used to execute pipelines described in a configuration file in the terminal:

mapsims_run example_config.toml

MapSims object¶

Create the simulator object with:

import mapsims
simulator = mapsims.from_config("example_config.toml")

This returns a MapSims object, then you can produce the output map with:

output_map = simulator.execute()

Python classes¶

Using instead the Python classes, we first need to create the custom component objects, as an example we will use all defaults options:

NSIDE = 16
cmb = mapsims.SOPrecomputedCMB(
    iteration_num=0,
    nside=NSIDE,
    lensed=False,
    aberrated=False,
    has_polarization=True,
    cmb_set=0,
    cmb_dir="mapsims/tests/data",
    input_units="uK_CMB",
)

Then we can create a SONoiseSimulator, the most important parameter is the scanning strategy, it can be either “classical” or “opportunistic”:

noise = mapsims.SONoiseSimulator(
    nside=NSIDE,
    return_uK_CMB=True,
    sensitivity_mode="baseline",
    apply_beam_correction=True,
    apply_kludge_correction=True,
    scanning_strategy="classical",
    LA_number_LF=1,
    LA_number_MF=4,
    LA_number_UHF=2,
    SA_years_LF=1,
    SA_one_over_f_mode="pessimistic",
)

Finally we can create the MapSim simulator object and pass the PySM custom component and the noise simulator as dictionaries, we can also specify any default model from PySM as a comma separated string, e.g. “d7,a1,s2”:

simulator = mapsims.MapSim(
    channels="all",
    nside=NSIDE,
    unit="uK_CMB",
    pysm_output_reference_frame="G",
    pysm_components_string="a1",
    pysm_custom_components={"cmb": cmb},
    other_components={"noise": noise},
)

and compute the output map using the execute method:

output_map = simulator.execute()

write instead directly output FITS maps to disk with:

simulator.execute(write_outputs=True)

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