aemulus_data package

Module contents

This file provides simple functions to get paths to various data.

aemulus_data.big_box_binned_mass_function(box, snapshot)

The binned mass function for a snapshot of a big box.

Units are Msun/h. Columns are M_low, M_high, Number, Total_Mass. To get the average mass of halos in a bin divide Total_Mass/Number.

Parameters:

• box (int) – Index of the big box; from 0-6.
• snapshot (int) – Index of the snapshot; from 0-9.

Returns:

Nbinsx4 array of binned mass function data.

Return type:

numpy.array

aemulus_data.big_box_binned_mass_function_covariance(box, snapshot)

The covariance matrix for the binned mass function for a snapshot of a big box.

Units are Msun/h.

Parameters:

• box (int) – Index of the big box; from 0-39.
• snapshot (int) – Index of the snapshot; from 0-9.

Returns:

NbinsxNbins symmetric covariance matrix.

Return type:

numpy.array

aemulus_data.building_box_binned_mass_function(box, snapshot)

The binned mass function for a snapshot of a box.

Units are Msun/h. Columns are M_low, M_high, Number, Total_Mass. To get the average mass of halos in a bin divide Total_Mass/Number.

Parameters:

• box (int) – Index of the simulation box; from 0-39.
• snapshot (int) – Index of the snapshot; from 0-9.

Returns:

Nbinsx4 array of binned mass function data.

Return type:

numpy.array

aemulus_data.building_box_binned_mass_function_covariance(box, snapshot)

The covariance matrix for the binned mass function for a snapshot of a simulation box.

Units are Msun/h.

Parameters:

• box (int) – Index of the simulation box; from 0-39.
• snapshot (int) – Index of the snapshot; from 0-9.

Returns:

NbinsxNbins symmetric covariance matrix.

Return type:

numpy.array

aemulus_data.building_box_cosmologies()

Cosmologies for the building boxes aka the aemulus simulations.

Columns are: Omega_bh^2 Omega_ch^2 w0 ns ln10As H0[km/s/Mpc] Neff sigma8.

Returns:40 by 8 array of the cosmologies for each simulation. Return type:numpy.array

aemulus_data.highres_box_binned_mass_function(box, snapshot)

The binned mass function for a snapshot of a highres box.

Units are Msun/h. Columns are M_low, M_high, Number, Mean_Mass.

Parameters:

• box (int) – Index of the medium box; 11 or 14.
• snapshot (int) – Index of the snapshot; from 0-13.

Returns:

Nbinsx4 array of binned mass function data.

Return type:

numpy.array

aemulus_data.highres_box_binned_mass_function_covariance(box, snapshot)

The covariance matrix for the binned mass function for a snapshot of a highres box.

Units are Msun/h.

Parameters:

• box (int) – Index of the medium box; 11 or 14.
• snapshot (int) – Index of the snapshot; from 0-13.

Returns:

symmetric covariance matrix.

Return type:

numpy.array

aemulus_data.highres_box_cosmologies()

Cosmologies for the highres boxes.

Note: this doesn’t contain sigma8.

Columns are: Omega_bh^2 Omega_ch^2 w0 ns ln10As H0[km/s/Mpc] Neff

Returns:40 by 7 array of the cosmologies for each simulation. Return type:numpy.array

aemulus_data.highres_scale_factors()

Scale factors of snapshots of the highres simulations.

Note: these are not the same scale factors as those of the building and test boxes.

Returns:Scale factors of highres snapshots. Return type:array

aemulus_data.individual_test_box_binned_mass_function(box, snapshot, realization)

The binned mass function for a snapshot of a test box.

Units are Msun/h. Columns are M_low, M_high, Number, Total_Mass. To get the average mass of halos in a bin divide Total_Mass/Number.

Parameters:

• box (int) – Index of the test box; from 0-6.
• snapshot (int) – Index of the snapshot; from 0-9.
• realization (int) – Index of the realization; from 0-4.

Returns:

Nbinsx4 array of binned mass function data.

Return type:

numpy.array

aemulus_data.individual_test_box_binned_mass_function_covariance(box, snapshot, realization)

The covariance matrix for the binned mass function for a snapshot of a test box.

Units are Msun/h.

Parameters:

• box (int) – Index of the test box; from 0-39.
• snapshot (int) – Index of the snapshot; from 0-9.
• realization (int) – Index of the realization; from 0-4.

Returns:

NbinsxNbins symmetric covariance matrix.

Return type:

numpy.array

aemulus_data.medium_box_binned_mass_function(box, snapshot)

The binned mass function for a snapshot of a medium box.

Units are Msun/h. Columns are M_low, M_high, Number, Total_Mass. To get the average mass of halos in a bin divide Total_Mass/Number.

Parameters:

• box (int) – Index of the medium box; from 0-6.
• snapshot (int) – Index of the snapshot; from 0-9.

Returns:

Nbinsx4 array of binned mass function data.

Return type:

numpy.array

aemulus_data.medium_box_binned_mass_function_covariance(box, snapshot)

The covariance matrix for the binned mass function for a snapshot of a medium box.

Units are Msun/h.

Parameters:

• box (int) – Index of the medium box; from 0-39.
• snapshot (int) – Index of the snapshot; from 0-9.

Returns:

NbinsxNbins symmetric covariance matrix.

Return type:

numpy.array

aemulus_data.scale_factors()

Scale factors of snapshots.

Returns:Scale factors of the snapshots. Return type:array

aemulus_data.test_box_binned_mass_function(box, snapshot)

The binned mass function for a snapshot of a test box.

Units are Msun/h. Columns are M_low, M_high, Number, Total_Mass. To get the average mass of halos in a bin divide Total_Mass/Number.

Parameters:

• box (int) – Index of the test box; from 0-6.
• snapshot (int) – Index of the snapshot; from 0-9.

Returns:

Nbinsx4 array of binned mass function data.

Return type:

numpy.array

aemulus_data.test_box_binned_mass_function_covariance(box, snapshot)

The covariance matrix for the binned mass function for a snapshot of a test box.

Units are Msun/h.

Parameters:

• box (int) – Index of the test box; from 0-39.
• snapshot (int) – Index of the snapshot; from 0-9.

Returns:

NbinsxNbins symmetric covariance matrix.

Return type:

numpy.array

aemulus_data.test_box_cosmologies()

Cosmologies for the test boxes.

Columns are: Omega_bh^2 Omega_ch^2 w0 ns ln10As H0[km/s/Mpc] Neff

Returns:7 by 8 array of the cosmologies for each simulation. Return type:numpy.array