docs: update

docs/build/html/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 4c494d747a2a3309a821e12e38277420
+tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/build/html/_downloads/1a23db2426a59e7579e865ba811a62e2/spacecraft_demo.py

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+import numpy as np
+import litebird_sim as lbs
+from astropy.time import Time
+import matplotlib.pylab as plt
+
+orbit = lbs.SpacecraftOrbit(start_time=Time("2023-01-01"))
+
+posvel = lbs.spacecraft_pos_and_vel(
+    orbit,
+    start_time=orbit.start_time,
+    time_span_s=3.15e7,  # One year
+    delta_time_s=86_400.0,  # One day
+)
+
+# posvel.positions_km is a N×3 array containing the XYZ position
+# of the spacecraft calculated every day for one year. We compute
+# the modulus of the position, which is of course the
+# Sun-LiteBIRD distance.
+sun_distance_km = np.linalg.norm(posvel.positions_km, axis=1)
+
+# We do the same with the velocities
+speed_km_s = np.linalg.norm(posvel.velocities_km_s, axis=1)
+
+# Plot distance and speed as functions of time
+_, ax = plt.subplots(nrows=2, ncols=1, figsize=(7, 7))
+
+ax[0].plot(sun_distance_km)
+ax[0].set_xlabel("Day")
+ax[0].set_ylabel("Distance [km]")
+
+ax[1].plot(speed_km_s)
+ax[1].set_xlabel("Day")
+ax[1].set_ylabel("Speed [km/s]")

docs/build/html/_downloads/2c527c73c72e71d690b18bccd1971129/thermalgain_demo.py

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+import numpy as np
+import litebird_sim as lbs
+from astropy.time import Time
+import matplotlib.pyplot as plt
+
+start_time = Time("2034-05-02")
+duration_s = 100
+sampling_freq_Hz = 1
+
+# Creating a list of detectors. The three detectors belong to two
+# different wafer groups.
+dets = [
+    lbs.DetectorInfo(
+        name="det_A_wafer_1", sampling_rate_hz=sampling_freq_Hz, wafer="wafer_1"
+    ),
+    lbs.DetectorInfo(
+        name="det_B_wafer_1", sampling_rate_hz=sampling_freq_Hz, wafer="wafer_1"
+    ),
+    lbs.DetectorInfo(
+        name="det_C_wafer_2", sampling_rate_hz=sampling_freq_Hz, wafer="wafer_2"
+    ),
+]
+
+# Defining the gain drift simulation parameters with no detector mismatch
+drift_params_no_mismatch = lbs.GainDriftParams(
+    drift_type=lbs.GainDriftType.THERMAL_GAIN,
+    focalplane_group="wafer",
+    detector_mismatch=0.0,
+)
+
+# Defining the gain drift simulation parameters with detector mismatch
+drift_params_with_mismatch = lbs.GainDriftParams(
+    drift_type=lbs.GainDriftType.THERMAL_GAIN,
+    focalplane_group="wafer",
+    detector_mismatch=1.0,
+)
+
+sim1 = lbs.Simulation(
+    start_time=start_time,
+    duration_s=duration_s,
+    random_seed=12345,
+)
+
+sim1.create_observations(
+    detectors=dets,
+    split_list_over_processes=False,
+    num_of_obs_per_detector=1,
+)
+
+sim1.observations[0].thermalgain_tod_no_mismatch = np.ones_like(
+    sim1.observations[0].tod
+)
+sim1.observations[0].thermalgain_tod_with_mismatch = np.ones_like(
+    sim1.observations[0].tod
+)
+
+# Generating the gain drift factors with no detector mismatch
+sim1.apply_gaindrift(
+    drift_params=drift_params_no_mismatch,
+    component="thermalgain_tod_no_mismatch",
+    user_seed=12345,
+)
+
+# Generating the gain drift factors with detector mismatch
+sim1.apply_gaindrift(
+    drift_params=drift_params_with_mismatch,
+    component="thermalgain_tod_with_mismatch",
+    user_seed=12345,
+)
+
+plt.figure(figsize=(8, 10))
+
+plt.subplot(211)
+for idx in range(sim1.observations[0].tod.shape[0]):
+    plt.plot(
+        sim1.observations[0].thermalgain_tod_no_mismatch[idx],
+        label=sim1.observations[0].name[idx],
+    )
+
+plt.xlabel("Time (in seconds)")
+plt.ylabel("Linear gain factor amplitude")
+plt.title("Thermal gain drift factor with no detector mismatch")
+plt.legend()
+
+plt.subplot(212)
+for idx in range(sim1.observations[0].tod.shape[0]):
+    plt.plot(
+        sim1.observations[0].thermalgain_tod_with_mismatch[idx],
+        label=sim1.observations[0].name[idx],
+    )
+
+plt.xlabel("Time (in seconds)")
+plt.ylabel("Linear gain factor amplitude")
+plt.title("Thermal gain drift factor with detector mismatch")
+plt.legend()
+plt.tight_layout()

docs/build/html/_downloads/57cb5d5e5a03f83dcbbc60b5819d0a7a/bandpass_demo.py

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+import litebird_sim as lbs
+import matplotlib.pylab as plt
+
+# Generate the «model» (i.e., ideal band, with no wiggles)
+band = lbs.BandPassInfo(
+    bandcenter_ghz=43.0,
+    bandwidth_ghz=10.0,
+    bandtype="top-hat-cosine",
+    normalize=True,
+    nsamples_inband=100,
+)
+plt.plot(band.freqs_ghz, band.weights, label="Ideal band")
+
+# Now generate a more realistic band with random wiggles
+new_weights = band.bandpass_resampling()
+plt.plot(
+    band.freqs_ghz,
+    new_weights,
+    label="Random realization",
+)
+
+plt.xlabel("Frequency [GHz]")
+plt.ylabel("Weight")
+plt.legend()

docs/build/html/_downloads/96d1d92116c317651ba8d0c45fe2859c/lingain_demo.py

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+import numpy as np
+import litebird_sim as lbs
+from astropy.time import Time
+import matplotlib.pyplot as plt
+
+start_time = Time("2034-05-02")
+duration_s = 4 * 24 * 3600
+sampling_freq_Hz = 1
+
+# Creating a list of detectors.
+dets = [
+    lbs.DetectorInfo(
+        name="det_A_wafer_1", sampling_rate_hz=sampling_freq_Hz, wafer="wafer_1"
+    ),
+    lbs.DetectorInfo(
+        name="det_B_wafer_1", sampling_rate_hz=sampling_freq_Hz, wafer="wafer_1"
+    ),
+]
+
+# Defining the gain drift simulation parameters
+drift_params = lbs.GainDriftParams(
+    drift_type=lbs.GainDriftType.LINEAR_GAIN,
+    calibration_period_sec=24 * 3600,
+)
+
+sim1 = lbs.Simulation(
+    start_time=start_time,
+    duration_s=duration_s,
+    random_seed=12345,
+)
+
+sim1.create_observations(
+    detectors=dets,
+    split_list_over_processes=False,
+    num_of_obs_per_detector=1,
+)
+
+sim1.observations[0].lingain_tod = np.ones_like(sim1.observations[0].tod)
+
+# Applying gain drift using the `Simulation` class method
+sim1.apply_gaindrift(
+    drift_params=drift_params,
+    component="lingain_tod",
+)
+
+plt.figure(figsize=(8, 5))
+
+time_domain = (
+    np.arange(sim1.observations[0].tod.shape[1]) / sampling_freq_Hz / 24 / 3600
+)
+
+for idx in range(sim1.observations[0].tod.shape[0]):
+    plt.plot(
+        time_domain,
+        sim1.observations[0].lingain_tod[idx],
+        label=sim1.observations[0].name[idx],
+    )
+
+plt.xlabel("Time (in days)")
+plt.ylabel("Linear gain factor amplitude")
+plt.legend()

docs/build/html/_downloads/db9d2aed004b9cdb4001394d56adf8c2/dipole_demo.py

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+from astropy.time import Time
+import numpy as np
+import litebird_sim as lbs
+import matplotlib.pylab as plt
+
+start_time = Time("2022-01-01")
+time_span_s = 120.0  # Two minutes
+sampling_hz = 20
+
+sim = lbs.Simulation(start_time=start_time, duration_s=time_span_s, random_seed=12345)
+
+# We pick a simple scanning strategy where the spin axis is aligned
+# with the Sun-Earth axis, and the spacecraft spins once every minute
+sim.set_scanning_strategy(
+    lbs.SpinningScanningStrategy(
+        spin_sun_angle_rad=np.deg2rad(0),
+        precession_rate_hz=0,
+        spin_rate_hz=1 / 60,
+        start_time=start_time,
+    ),
+    delta_time_s=5.0,
+)
+
+# We simulate an instrument whose boresight is perpendicular to
+# the spin axis.
+sim.set_instrument(
+    lbs.InstrumentInfo(
+        boresight_rotangle_rad=0.0,
+        spin_boresight_angle_rad=np.deg2rad(90),
+        spin_rotangle_rad=np.deg2rad(75),
+    )
+)
+
+# A simple detector looking along the boresight direction
+det = lbs.DetectorInfo(
+    name="Boresight_detector",
+    sampling_rate_hz=sampling_hz,
+    bandcenter_ghz=100.0,
+)
+
+(obs,) = sim.create_observations(detectors=[det])
+
+sim.prepare_pointings()
+
+# Simulate the orbit of the spacecraft and compute positions and
+# velocities
+orbit = lbs.SpacecraftOrbit(obs.start_time)
+pos_vel = lbs.spacecraft_pos_and_vel(orbit, obs, delta_time_s=60.0)
+
+t = obs.get_times()
+t -= t[0]  # Make `t` start from zero
+
+# Create two plots: the first shows the full two-minute time span, and
+# the second one shows a zoom over the very first points of the TOD.
+_, axes = plt.subplots(nrows=2, ncols=1, figsize=(10, 12))
+
+# Make a plot of the TOD using all the dipole types
+for type_idx, dipole_type in enumerate(lbs.DipoleType):
+    obs.tod[0][:] = 0  # Reset the TOD
+    lbs.add_dipole_to_observations(obs, pos_vel, dipole_type=dipole_type)
+
+    axes[0].plot(t, obs.tod[0], label=str(dipole_type))
+    axes[1].plot(t[0:20], obs.tod[0][0:20], label=str(dipole_type))
+
+axes[0].set_xlabel("Time [s]")
+axes[0].set_ylabel("Signal [K]")
+axes[1].set_xlabel("Time [s]")
+axes[1].set_ylabel("Signal [K]")
+axes[1].legend()