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CoDICE l2 fix intensity calculations for invalid half_spin_per_esa_steps #2631

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Merged
lacoak21 merged 2 commits into from
Jan 28, 2026
Merged

CoDICE l2 fix intensity calculations for invalid half_spin_per_esa_steps #2631

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3cb177c
only consider valid half spins
lacoak21 Jan 28, 2026
b2081e6
pr comments
lacoak21 Jan 28, 2026
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7 changes: 4 additions & 3 deletions imap_processing/codice/codice_l1a_lo_angular.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@

from imap_processing.cdf.imap_cdf_manager import ImapCdfAttributes
from imap_processing.codice import constants
from imap_processing.codice.constants import HALF_SPIN_FILLVAL
from imap_processing.codice.decompress import decompress
from imap_processing.codice.utils import (
CODICEAPID,
Expand Down Expand Up @@ -197,12 +198,12 @@ def l1a_lo_angular(unpacked_dataset: xr.Dataset, lut_file: Path) -> xr.Dataset:
]
voltage_data = sci_lut_data["esa_sweep_tab"][f"{esa_table_number}"]

# If data size is less than 128, pad with nan to make it 128
# If data size is less than 128, pad with fillval to make it 128
half_spin_per_esa_step = sci_lut_data["lo_stepping_tab"]["row_number"].get("data")
if len(half_spin_per_esa_step) < constants.NUM_ESA_STEPS:
pad_size = constants.NUM_ESA_STEPS - len(half_spin_per_esa_step)
half_spin_per_esa_step = np.concatenate(
(np.array(half_spin_per_esa_step), np.full(pad_size, np.nan))
(np.array(half_spin_per_esa_step), np.full(pad_size, HALF_SPIN_FILLVAL))
)
# TODO: Handle epoch dependent acquisition time and half spin per esa step
# For now, just tile the same array for all epochs.
Expand All @@ -228,7 +229,7 @@ def l1a_lo_angular(unpacked_dataset: xr.Dataset, lut_file: Path) -> xr.Dataset:
species_data = species_data.astype(np.float64)
species_data[species_mask] = np.nan
# Set half_spin_per_esa_step to (fillval) where nso_mask is True
half_spin_per_esa_step[nso_mask] = 63
half_spin_per_esa_step[nso_mask] = HALF_SPIN_FILLVAL
# Set acquisition_time_per_step to nan where nso_mask is True
acquisition_time_per_step[nso_mask] = np.nan
# ========= Get Epoch Time Data ===========
Expand Down
7 changes: 4 additions & 3 deletions imap_processing/codice/codice_l1a_lo_counters_aggregated.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@

from imap_processing.cdf.imap_cdf_manager import ImapCdfAttributes
from imap_processing.codice import constants
from imap_processing.codice.constants import HALF_SPIN_FILLVAL
from imap_processing.codice.decompress import decompress
from imap_processing.codice.utils import (
ViewTabInfo,
Expand Down Expand Up @@ -114,12 +115,12 @@ def l1a_lo_counters_aggregated(
-1, esa_step, num_variables, spin_sector_pairs
)

# If data size is less than 128, pad with nan to make it 128
# If data size is less than 128, pad with fillval to make it 128
half_spin_per_esa_step = sci_lut_data["lo_stepping_tab"]["row_number"].get("data")
if len(half_spin_per_esa_step) < constants.NUM_ESA_STEPS:
pad_size = constants.NUM_ESA_STEPS - len(half_spin_per_esa_step)
half_spin_per_esa_step = np.concatenate(
(np.array(half_spin_per_esa_step), np.full(pad_size, np.nan))
(np.array(half_spin_per_esa_step), np.full(pad_size, HALF_SPIN_FILLVAL))
)
# TODO: Handle epoch dependent acquisition time and half spin per esa step
# For now, just tile the same array for all epochs.
Expand All @@ -145,7 +146,7 @@ def l1a_lo_counters_aggregated(
counters_data = counters_data.astype(np.float64)
counters_data[counters_mask] = np.nan
# Set half_spin_per_esa_step to (fillval) where nso_mask is True
half_spin_per_esa_step[nso_mask] = 63
half_spin_per_esa_step[nso_mask] = HALF_SPIN_FILLVAL
# Set acquisition_time_per_step to nan where nso_mask is True
acquisition_time_per_step[nso_mask] = np.nan

Expand Down
7 changes: 4 additions & 3 deletions imap_processing/codice/codice_l1a_lo_counters_singles.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@

from imap_processing.cdf.imap_cdf_manager import ImapCdfAttributes
from imap_processing.codice import constants
from imap_processing.codice.constants import HALF_SPIN_FILLVAL
from imap_processing.codice.decompress import decompress
from imap_processing.codice.utils import (
ViewTabInfo,
Expand Down Expand Up @@ -111,12 +112,12 @@ def l1a_lo_counters_singles(unpacked_dataset: xr.Dataset, lut_file: Path) -> xr.
.transpose(0, 1, 3, 2)
)

# If data size is less than 128, pad with nan to make it 128
# If data size is less than 128, pad with fillval to make it 128
half_spin_per_esa_step = sci_lut_data["lo_stepping_tab"]["row_number"].get("data")
if len(half_spin_per_esa_step) < constants.NUM_ESA_STEPS:
pad_size = constants.NUM_ESA_STEPS - len(half_spin_per_esa_step)
half_spin_per_esa_step = np.concatenate(
(np.array(half_spin_per_esa_step), np.full(pad_size, np.nan))
(np.array(half_spin_per_esa_step), np.full(pad_size, HALF_SPIN_FILLVAL))
)
# TODO: Handle epoch dependent acquisition time and half spin per esa step
# For now, just tile the same array for all epochs.
Expand All @@ -142,7 +143,7 @@ def l1a_lo_counters_singles(unpacked_dataset: xr.Dataset, lut_file: Path) -> xr.
counters_data = counters_data.astype(np.float64)
counters_data[counters_mask] = np.nan
# Set half_spin_per_esa_step to (fillval) where nso_mask is True
half_spin_per_esa_step[nso_mask] = 63
half_spin_per_esa_step[nso_mask] = HALF_SPIN_FILLVAL
# Set acquisition_time_per_step to nan where nso_mask is True
acquisition_time_per_step[nso_mask] = np.nan

Expand Down
7 changes: 4 additions & 3 deletions imap_processing/codice/codice_l1a_lo_priority.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@

from imap_processing.cdf.imap_cdf_manager import ImapCdfAttributes
from imap_processing.codice import constants
from imap_processing.codice.constants import HALF_SPIN_FILLVAL
from imap_processing.codice.decompress import decompress
from imap_processing.codice.utils import (
CODICEAPID,
Expand Down Expand Up @@ -133,12 +134,12 @@ def l1a_lo_priority(unpacked_dataset: xr.Dataset, lut_file: Path) -> xr.Dataset:
num_packets, num_species, esa_steps, collapse_shape[0]
)

# If data size is less than 128, pad with nan to make it 128
# If data size is less than 128, pad with fillval to make it 128
half_spin_per_esa_step = sci_lut_data["lo_stepping_tab"]["row_number"].get("data")
if len(half_spin_per_esa_step) < constants.NUM_ESA_STEPS:
pad_size = constants.NUM_ESA_STEPS - len(half_spin_per_esa_step)
half_spin_per_esa_step = np.concatenate(
(np.array(half_spin_per_esa_step), np.full(pad_size, np.nan))
(np.array(half_spin_per_esa_step), np.full(pad_size, HALF_SPIN_FILLVAL))
)

# TODO: Handle epoch dependent acquisition time and half spin per esa step
Expand All @@ -165,7 +166,7 @@ def l1a_lo_priority(unpacked_dataset: xr.Dataset, lut_file: Path) -> xr.Dataset:
species_data = species_data.astype(np.float64)
species_data[species_mask] = np.nan
# Set half_spin_per_esa_step to (fillval) where nso_mask is True
half_spin_per_esa_step[nso_mask] = 63
half_spin_per_esa_step[nso_mask] = HALF_SPIN_FILLVAL
# Set acquisition_time_per_step to nan where nso_mask is True
acquisition_time_per_step[nso_mask] = np.nan

Expand Down
7 changes: 4 additions & 3 deletions imap_processing/codice/codice_l1a_lo_species.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@

from imap_processing.cdf.imap_cdf_manager import ImapCdfAttributes
from imap_processing.codice import constants
from imap_processing.codice.constants import HALF_SPIN_FILLVAL
from imap_processing.codice.decompress import decompress
from imap_processing.codice.utils import (
CODICEAPID,
Expand Down Expand Up @@ -121,12 +122,12 @@ def l1a_lo_species(unpacked_dataset: xr.Dataset, lut_file: Path) -> xr.Dataset:
num_packets, num_species, esa_steps, *collapsed_shape
)

# If data size is less than 128, pad with nan to make it 128
# If data size is less than 128, pad with fillval to make it 128
half_spin_per_esa_step = sci_lut_data["lo_stepping_tab"]["row_number"].get("data")
if len(half_spin_per_esa_step) < constants.NUM_ESA_STEPS:
pad_size = constants.NUM_ESA_STEPS - len(half_spin_per_esa_step)
half_spin_per_esa_step = np.concatenate(
(np.array(half_spin_per_esa_step), np.full(pad_size, np.nan))
(np.array(half_spin_per_esa_step), np.full(pad_size, HALF_SPIN_FILLVAL))
)

acquisition_time_per_step = calculate_acq_time_per_step(
Expand Down Expand Up @@ -155,7 +156,7 @@ def l1a_lo_species(unpacked_dataset: xr.Dataset, lut_file: Path) -> xr.Dataset:
species_data = species_data.astype(np.float64)
species_data[species_mask] = np.nan
# Set half_spin_per_esa_step to (fillval) where nso_mask is True
half_spin_per_esa_step[nso_mask] = 63
half_spin_per_esa_step[nso_mask] = HALF_SPIN_FILLVAL
# Set acquisition_time_per_step to nan where nso_mask is True
acquisition_time_per_step[nso_mask] = np.nan

Expand Down
15 changes: 12 additions & 3 deletions imap_processing/codice/codice_l2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
from imap_processing.cdf.utils import load_cdf
from imap_processing.codice.constants import (
GAIN_ID_TO_STR,
HALF_SPIN_FILLVAL,
HI_L2_ELEVATION_ANGLE,
HI_OMNI_VARIABLE_NAMES,
HI_SECTORED_VARIABLE_NAMES,
Expand Down Expand Up @@ -323,8 +324,14 @@ def compute_geometric_factors(
raise ValueError("Dataset is missing Logical_file_id attribute.")
processing_date = datetime.datetime.strptime(start_date.split("_")[4], "%Y%m%d")
date_switch = datetime.datetime(2025, 11, 24)
# Only consider valid half spins
valid_half_spin = half_spin_per_esa_step != HALF_SPIN_FILLVAL
if processing_date < date_switch:
modes = (half_spin_per_esa_step > rgfo_half_spin) & (rgfo_half_spin > 0)
modes = (
valid_half_spin
& (half_spin_per_esa_step > rgfo_half_spin)
& (rgfo_half_spin > 0)
)
else:
# After November 24th, 2025, we no longer apply reduced geometric factors;
# always use the full geometric factor lookup.
Expand Down Expand Up @@ -582,8 +589,10 @@ def process_lo_angular_intensity(
# is odd, the configuration is B.
# TODO handle when half_spin_per_esa_step changes in the middle of the dataset
half_spin_per_esa_step = dataset["half_spin_per_esa_step"].data[0]
a_inds = np.where(half_spin_per_esa_step % 2 == 0)[0]
b_inds = np.where(half_spin_per_esa_step % 2 == 1)[0]
# only consider valid half spin values
valid_half_spin = half_spin_per_esa_step != HALF_SPIN_FILLVAL
a_inds = np.nonzero(valid_half_spin & (half_spin_per_esa_step % 2 == 0))[0]
b_inds = np.nonzero(valid_half_spin & (half_spin_per_esa_step % 2 == 1))[0]

position_index = position_index_to_adjust
for species in species_list:
Expand Down
2 changes: 2 additions & 0 deletions imap_processing/codice/constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1054,3 +1054,5 @@
318.39,
]
)

HALF_SPIN_FILLVAL = 63