# Colab Setup (Run this first)

!pip install litebird_sim rich

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[notice] A new release of pip is available: 26.1.2 -> 26.2.1
[notice] To update, run: pip install --upgrade pip

Band integration with a Jones matrix#

To run this notebook, you have several options:

If you are running this under Binder, you should already be set!
If you are running this under Google Colab, be sure to run the cell with !pip install… that is right above the title.
If you are running this locally, you should first create and activate a new virtual environment with the commands
```
python -m venv ./my_venv
source ./my_venv/bin/activate
```
(you can use Conda environments, if you prefer) and install Jupyter and litebird_sim in it:
```
pip install jupyter litebird_sim
```
If you have a local copy of the litebird_sim repository cloned from litebird/litebird_sim (e.g., because you’re part of the Simulation Team!), you can use a development install instead:
```
cd /my/local/copy/litebird_sim
pip install -e .
```

Import Modules#

import healpy as hp
import matplotlib.pyplot as plt
import numpy as np

import litebird_sim as lbs
from litebird_sim.input_sky import SkyGenerator, SkyGenerationParams
from litebird_sim.units import Units

Define Parameters#

start_time = 0
time_span_s = 100 * 24 * 3600
nside = 64
imo_location = "schema.json"
imo = lbs.Imo(flatfile_location=lbs.PTEP_IMO_LOCATION)
imo_version = "vPTEP"

Create Simulation#

sim = lbs.Simulation(
    start_time=start_time, duration_s=time_span_s, random_seed=0, imo=imo
)

comm = sim.mpi_comm

[2026-09-03 17:22:42,232 WARNING MPI#0000] MPI layer is already initialized. Reinitializing the entire RNG hierarchy.
[2026-09-03 17:22:42,232 WARNING MPI#0000] MPI layer is already initialized. Reinitializing the entire RNG hierarchy.

Define Frequency Channel#

telescope = "LFT"
channel = "L4-140"

channelinfo = lbs.FreqChannelInfo.from_imo(
    url=f"/releases/{imo_version}/satellite/{telescope}/{channel}/channel_info",
    imo=imo,
)

Define Scanning Strategy#

sim.set_scanning_strategy(
    imo_url=f"/releases/{imo_version}/satellite/scanning_parameters/",
)

Define Instrument#

instrument_info = lbs.InstrumentInfo.from_imo(
    imo,
    url=f"/releases/{imo_version}/satellite/{telescope}/instrument_info",
)

sim.set_instrument(instrument_info)

hwp_radpsec = instrument_info.hwp_rpm * 2 * np.pi / 60

Set HWP#

# the file mock_jones_params actually includes ideal HWP parameters
# you can change them to see the influence on the TOD and output maps
jones_params_filepath = "mock_jones_params.csv"

with open(jones_params_filepath, "r", encoding="utf-8") as jones_params_file:
    sim.set_hwp(
        lbs.NonIdealHWP(
            hwp_radpsec,
            harmonic_expansion=True,
            calculus=lbs.HWPFormalism.JONES,
            jones_parameters=lbs.HWPJonesParams.from_stream(jones_params_file),
        )
    )

Define Detectors#

det = lbs.DetectorInfo.from_imo(
    url=f"/releases/{imo_version}/satellite/{telescope}/{channel}/{channelinfo.detector_names[0]}/detector_info",
    imo=imo,
)

# This is usually not needed. PTEP IMo has no pointing_theta_phi_psi_deg value, so we're setting some values in here so that the code does not break.
det.pointing_theta_phi_psi_deg = [0, 0, 0]
# We're also reducing the sampling rate so that the simulation goes faster.
det.sampling_rate_hz = 1
# Also reducing bandwidth so that there's fewer frequencies for the band integration.
det.bandwidth_ghz = 2.0

Create Observations and Prepare Pointings#

(obs,) = sim.create_observations(
    detectors=[det],
)

sim.prepare_pointings(append_to_report=False)
sim.precompute_pointings()

Generate Input Sky Maps#

params = SkyGenerationParams(
    nside=64,
    units=Units.MJy_over_sr,
    output_type="map",
    make_cmb=True,
    seed_cmb=12345,
    make_fg=True,
    make_dipole=True,
    fg_models=["d1", "s1"],
    apply_beam=True,
)

data = np.loadtxt(
    jones_params_filepath,
    delimiter=",",
    dtype=object,
    unpack=True,
    skiprows=1,
    comments="#",
)

frequencies = data[0, :].astype(np.float64)
indices = np.where(
    (frequencies >= det.bandcenter_ghz - det.bandwidth_ghz / 2)
    & (frequencies <= det.bandcenter_ghz + det.bandwidth_ghz / 2)
)[0]

frequencies_ghz = frequencies[indices]
fwhm_arcmin = 37.805193
fwhm_rad = np.radians(fwhm_arcmin / 60.0)

print("frequencies for band integration:", frequencies_ghz)

frequencies for band integration: [139.4117647 139.9926471 140.5735294]

sky_gen = SkyGenerator(
    parameters=params,
    frequencies_ghz=frequencies_ghz,
    fwhm_rad=fwhm_rad,
)
sky_map = sky_gen.execute()

[2026-09-03 17:22:44,140 INFO MPI#0000] Generating CMB (frequency mode)...
[2026-09-03 17:22:44,168 INFO MPI#0000] Generating Foregrounds (frequency mode)...
[2026-09-03 17:22:44,168 INFO MPI#0000] Retrieve data for pysm_2/dust_t_new.fits (if not cached already)
[2026-09-03 17:22:44,170 INFO MPI#0000] NSIDE = 512
[2026-09-03 17:22:44,170 INFO MPI#0000] ORDERING = RING in fits file
[2026-09-03 17:22:44,170 INFO MPI#0000] INDXSCHM = IMPLICIT
[2026-09-03 17:22:44,204 INFO MPI#0000] Retrieve data for pysm_2/dust_q_new.fits (if not cached already)
[2026-09-03 17:22:44,205 INFO MPI#0000] NSIDE = 512
[2026-09-03 17:22:44,206 INFO MPI#0000] ORDERING = RING in fits file
[2026-09-03 17:22:44,206 INFO MPI#0000] INDXSCHM = IMPLICIT
[2026-09-03 17:22:44,232 INFO MPI#0000] Retrieve data for pysm_2/dust_u_new.fits (if not cached already)
[2026-09-03 17:22:44,233 INFO MPI#0000] NSIDE = 512
[2026-09-03 17:22:44,234 INFO MPI#0000] ORDERING = RING in fits file
[2026-09-03 17:22:44,234 INFO MPI#0000] INDXSCHM = IMPLICIT
[2026-09-03 17:22:44,260 INFO MPI#0000] Retrieve data for pysm_2/dust_beta.fits (if not cached already)
[2026-09-03 17:22:44,261 INFO MPI#0000] NSIDE = 512
[2026-09-03 17:22:44,261 INFO MPI#0000] ORDERING = RING in fits file
[2026-09-03 17:22:44,261 INFO MPI#0000] INDXSCHM = IMPLICIT
[2026-09-03 17:22:44,287 INFO MPI#0000] Retrieve data for pysm_2/dust_temp.fits (if not cached already)
[2026-09-03 17:22:44,288 INFO MPI#0000] NSIDE = 512
[2026-09-03 17:22:44,288 INFO MPI#0000] ORDERING = RING in fits file
[2026-09-03 17:22:44,288 INFO MPI#0000] INDXSCHM = IMPLICIT
[2026-09-03 17:22:44,314 INFO MPI#0000] Retrieve data for pysm_2/synch_t_new.fits (if not cached already)
[2026-09-03 17:22:44,315 INFO MPI#0000] NSIDE = 512
[2026-09-03 17:22:44,316 INFO MPI#0000] ORDERING = RING in fits file
[2026-09-03 17:22:44,316 INFO MPI#0000] INDXSCHM = IMPLICIT
[2026-09-03 17:22:44,341 INFO MPI#0000] Retrieve data for pysm_2/synch_q_new.fits (if not cached already)
[2026-09-03 17:22:44,342 INFO MPI#0000] NSIDE = 512
[2026-09-03 17:22:44,342 INFO MPI#0000] ORDERING = RING in fits file
[2026-09-03 17:22:44,343 INFO MPI#0000] INDXSCHM = IMPLICIT
[2026-09-03 17:22:44,368 INFO MPI#0000] Retrieve data for pysm_2/synch_u_new.fits (if not cached already)
[2026-09-03 17:22:44,369 INFO MPI#0000] NSIDE = 512
[2026-09-03 17:22:44,369 INFO MPI#0000] ORDERING = RING in fits file
[2026-09-03 17:22:44,369 INFO MPI#0000] INDXSCHM = IMPLICIT
[2026-09-03 17:22:44,395 INFO MPI#0000] Retrieve data for pysm_2/synch_beta.fits (if not cached already)
[2026-09-03 17:22:44,396 INFO MPI#0000] NSIDE = 512
[2026-09-03 17:22:44,396 INFO MPI#0000] ORDERING = RING in fits file
[2026-09-03 17:22:44,396 INFO MPI#0000] INDXSCHM = IMPLICIT
[2026-09-03 17:22:46,002 INFO MPI#0000] Generating Dipole (frequency mode)...

Generate HwpSys object and Set its parameters#

Fill TOD and perform Mapmaking on-the-fly#

lbs.scan_map_in_observations(
    observations=[obs],
    maps=sky_map,
    integrate_in_band=True,
)

output_maps = sim.make_binned_map(nside=nside).binned_map
input_maps = np.trapezoid(sky_map.values, frequencies_ghz * 1e9, axis=0) / (  # type: ignore[no-matching-overload]
    det.bandwidth_ghz * 1e9
)

Show Output Maps#

plt.figure(figsize=(32, 10))
stokes_parameters = ["T", "Q", "U"]
for i in range(3):
    hp.mollview(
        input_maps[i],
        cmap="jet",
        sub=(2, 3, i + 1),
        title="Input " + stokes_parameters[i],
    )

    hp.mollview(
        output_maps[i],
        cmap="jet",
        sub=(2, 3, i + 4),
        title="Leakage " + stokes_parameters[i],
    )

../_images/5f4fed1cd6cf6b6060a1c9ebe982bace4fe55293e22bf6969415273e7235fafc.png