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307 | @timed_function("process_ngrm_electron_fluxes")
def process_ngrm_electron_fluxes(
satellite: Literal["EDRS-C", "S6-MF", "MTG-S1", "MTG-I1"],
raw_data_path: str | Path,
processed_data_path: str | Path,
start_time: datetime,
end_time: datetime,
num_cores: int = 32,
bin_cadence: timedelta = timedelta(seconds=10),
skip_existing: bool = True, # noqa: FBT001, FBT002,
client_id: str | None = None,
client_secret: str | None = None,
saving_strategy: ep.SavingStrategy | None = None,
*,
calculate_Lstar: bool = True,
) -> None:
"""Process ESA NGRM electron flux data for the given satellite into omnidirectional fluxes and PSD.
This downloads the NGRM L1d CSV data for the given satellite from the ESA SWE HAPI service,
extracts the differential electron flux channels, time, ECI position, and L-shell, converts
the time stamps to POSIX time and the ECI position to GEO coordinates, and combines the flux
channels into a single omnidirectional differential flux variable (converted to
(cm^2 s keV)^-1 units with negative values clipped to zero). The data is then time-binned to
`bin_cadence`, magnetic field model quantities (B_Calc, B_Eq, MLT_Eq, R_Eq, Alpha_Eq, L_m, and
optionally L_star) are computed using the T89 model for a fixed set of local pitch angles, the
pitch angle distribution (FEDU) and phase space density (PSD) are derived from the omnidirectional
flux, and all resulting variables are saved to disk (appending to existing files) using either the
provided `saving_strategy` or a default daily/monthly strategy depending on the satellite.
Args:
satellite (Literal["EDRS-C", "S6-MF", "MTG-S1", "MTG-I1"]): Identifier of the NGRM-carrying
satellite to process.
raw_data_path (str | Path): Base directory used for downloading and locating the raw NGRM CSV files.
processed_data_path (str | Path): Base directory in which the processed output files are saved.
start_time (datetime): Start of the time range to process.
end_time (datetime): End of the time range to process.
num_cores (int, optional): Number of CPU cores used for the magnetic field computations. Defaults to 32.
bin_cadence (timedelta, optional): Time binning cadence applied to the extracted variables.
Defaults to timedelta(seconds=10).
skip_existing (bool, optional): If True, skip downloading files that already exist locally.
Defaults to True.
client_id (str | None, optional): Client ID for the ESA SWE authentication. If None, it is read
from the `CLIENT_ID` environment variable. Defaults to None.
client_secret (str | None, optional): Client secret for the ESA SWE authentication. If None, it
is read from the `CLIENT_SECRET` environment variable. Defaults to None.
saving_strategy (ep.SavingStrategy | None, optional): Strategy used to save the processed
variables. If None, a `DailyLEORBStrategy` is used for "S6-MF" and a `MonthlyRBStrategy`
for all other satellites. Defaults to None.
calculate_Lstar (bool, optional): If True, also compute the L* magnetic field quantity.
Defaults to True.
Raises:
ValueError: If `client_id` or `client_secret` is not provided and not available via the
`CLIENT_ID`/`CLIENT_SECRET` environment variables.
"""
data_path_stem = f"{raw_data_path}/NGRM/{satellite}/YYYY/MM/"
file_name_stem = f"{satellite}_ngrm_YYYYMMDD_L1d.csv"
if client_id is None:
client_id = os.environ.get("CLIENT_ID")
if client_secret is None:
client_secret = os.environ.get("CLIENT_SECRET")
if client_id is None:
msg = "Client ID not found! Either load it from environment variables or pass it as an argument."
raise ValueError(msg)
if client_secret is None:
msg = "Client secret not found! Either load it from environment variables or pass it as an argument."
raise ValueError(msg)
ep.download(
start_time,
end_time,
save_path=data_path_stem,
file_cadence="daily",
download_url=SATELLITE_TO_ID[satellite],
file_name_stem="",
rename_file_name_stem=file_name_stem,
authentication_info=(client_id, client_secret),
method="esa_swe",
skip_existing=skip_existing,
)
flux_unit = typing.cast("u.Unit", (u.cm**2 * u.s * u.sr * u.MeV) ** (-1))
extraction_infos = [
ep.ExtractionInfo(result_key="Epoch_iso", name_or_column="Time", unit=u.dimensionless_unscaled),
ep.ExtractionInfo(
result_key="FEDO_ch1", name_or_column="Differential electron flux (0.18 MeV)", unit=flux_unit
),
ep.ExtractionInfo(
result_key="FEDO_ch2", name_or_column="Differential electron flux (0.27 MeV)", unit=flux_unit
),
ep.ExtractionInfo(
result_key="FEDO_ch3", name_or_column="Differential electron flux (0.40 MeV)", unit=flux_unit
),
ep.ExtractionInfo(
result_key="FEDO_ch4", name_or_column="Differential electron flux (0.60 MeV)", unit=flux_unit
),
ep.ExtractionInfo(
result_key="FEDO_ch5", name_or_column="Differential electron flux (0.88 MeV)", unit=flux_unit
),
ep.ExtractionInfo(
result_key="FEDO_ch6", name_or_column="Differential electron flux (1.30 MeV)", unit=flux_unit
),
ep.ExtractionInfo(
result_key="FEDO_ch7", name_or_column="Differential electron flux (1.93 MeV)", unit=flux_unit
),
ep.ExtractionInfo(
result_key="FEDO_ch8", name_or_column="Differential electron flux (2.90 MeV)", unit=flux_unit
),
ep.ExtractionInfo(
result_key="FEDO_ch9", name_or_column="Differential electron flux (3.40 MeV)", unit=flux_unit
),
ep.ExtractionInfo(
result_key="FEDO_ch10", name_or_column="Differential electron flux (4.00 MeV)", unit=flux_unit
),
ep.ExtractionInfo(result_key="x_ECI", name_or_column="X", unit=u.km),
ep.ExtractionInfo(result_key="y_ECI", name_or_column="Y", unit=u.km),
ep.ExtractionInfo(result_key="z_ECI", name_or_column="Z", unit=u.km),
ep.ExtractionInfo(result_key="L", name_or_column="L", unit=ep.units.RE),
]
try:
variables = ep.extract_variables_from_files(
start_time,
end_time,
file_cadence="daily",
data_path=data_path_stem,
file_name_stem=file_name_stem,
extraction_infos=extraction_infos,
pd_read_csv_kwargs={"index_col": False},
)
except Exception:
logger.exception(f"Error extracting variables for {satellite}")
return
time_format = "%Y-%m-%dT%H:%M:%S.%fZ" if satellite in ["MTG-I1", "MTG-S1"] else "%Y-%m-%dT%H:%M:%SZ"
# convert iso strings to posixtime
datetimes = ep.processing.convert_string_to_datetime(variables["Epoch_iso"], time_format=time_format)
variables["Epoch"] = ep.Variable(
data=np.asarray([t.timestamp() for t in datetimes]), original_unit=ep.units.posixtime
)
del variables["Epoch_iso"]
# convert ECI coordinates to GEO using astropy
coords_ECI = GCRS(
CartesianRepresentation(
x=variables["x_ECI"].get_data(), y=variables["y_ECI"].get_data(), z=variables["z_ECI"].get_data()
),
obstime=datetimes,
)
coords_ITRS = coords_ECI.transform_to(ITRS(obstime=datetimes))
xgeo_data = np.stack((coords_ITRS.x, coords_ITRS.y, coords_ITRS.z)).T
variables["xGEO"] = ep.Variable(data=xgeo_data.value, original_unit=u.km) # ty:ignore[unresolved-attribute]
del variables["x_ECI"], variables["y_ECI"], variables["z_ECI"]
# create flux variable
flux_data = np.stack(
[
variables["FEDO_ch1"].get_data(),
variables["FEDO_ch2"].get_data(),
variables["FEDO_ch3"].get_data(),
variables["FEDO_ch4"].get_data(),
variables["FEDO_ch5"].get_data(),
variables["FEDO_ch6"].get_data(),
variables["FEDO_ch7"].get_data(),
variables["FEDO_ch8"].get_data(),
variables["FEDO_ch9"].get_data(),
variables["FEDO_ch10"].get_data(),
]
).T.astype(np.float64)
# convert to proper omnidirectional units
flux_data = flux_data * 4 * np.pi
variables["FEDO"] = ep.Variable(data=flux_data, original_unit=flux_unit * u.sr)
for i in range(1, 11):
del variables[f"FEDO_ch{i}"]
variables["FEDO"].apply_thresholds_on_data(lower_threshold=0)
variables["FEDO"].convert_to_unit((u.cm**2 * u.s * u.keV) ** (-1))
# get energies
variables["Energy"] = ep.Variable(data=np.asarray(NGRM_ENERGIES), original_unit=u.MeV)
time_bin_methods = {
"Energy": ep.TimeBinMethod.Repeat,
"FEDO": ep.TimeBinMethod.NanMedian,
"FEDU": ep.TimeBinMethod.NanMedian,
"xGEO": ep.TimeBinMethod.NanMean,
"L": ep.TimeBinMethod.NanMean,
}
binned_time_var = ep.processing.bin_by_time(
variables["Epoch"], variables, time_bin_methods, bin_cadence, start_time=start_time, end_time=end_time
)
pa_local_data = np.tile(np.arange(5, 91, 5), (len(binned_time_var.get_data()), 1)).astype(np.float64)
variables["PA_local_FEDU"] = ep.Variable(data=pa_local_data, original_unit=u.deg)
variables_to_compute: ep.processing.VariableRequest = [
("B_Calc", "T89"),
("B_Eq", "T89"),
("MLT_Eq", "T89"),
("B_Eq", "T89"),
("R_Eq", "T89"),
("Alpha_Eq", "T89"),
("L_m", "T89"),
]
if calculate_Lstar:
variables_to_compute.append(("L_star", "T89")) # ty:ignore[invalid-argument-type]
magnetic_field_variables = ep.processing.compute_magnetic_field_variables(
time_var=binned_time_var,
xgeo_var=variables["xGEO"],
energy_var=variables["Energy"],
pa_local_var=variables["PA_local_FEDU"],
particle_species="electron",
variables_to_compute=variables_to_compute,
irbem_options=ep.processing.magnetic_field_utils.IrbemOptions(
lstar_quantity=ep.processing.magnetic_field_utils.LstarQuantity.LSTAR
if calculate_Lstar
else ep.processing.magnetic_field_utils.LstarQuantity.NONE,
),
num_cores=num_cores,
)
variables |= magnetic_field_variables
FEDU_var = ep.processing.construct_pitch_angle_distribution(
variables["FEDO"], variables["PA_local_FEDU"], magnetic_field_variables["Alpha_Eq_T89"]
)
FEDU_var.apply_thresholds_on_data(lower_threshold=0)
psd_var = ep.processing.compute_phase_space_density(FEDU_var, variables["Energy"], particle_species="electron")
variables_to_save: dict[ep.typing.InternalName, ep.Variable] = {
"Epoch": binned_time_var,
"FEDU": FEDU_var,
# "FEDO": variables["FEDO"], disabled for now, since Alpha_range is missing
"Energy_FEDU": variables["Energy"],
"Alpha": variables["PA_local_FEDU"],
"Alpha_Eq": magnetic_field_variables["Alpha_Eq_T89"],
"R_Eq": magnetic_field_variables["R_Eq_T89"],
"MLT": magnetic_field_variables["MLT_Eq_T89"],
"L_m": magnetic_field_variables["L_m_T89"],
"L_star": magnetic_field_variables["L_star_T89"],
"B_Calc": magnetic_field_variables["B_Calc_T89"],
"B_Eq": magnetic_field_variables["B_Eq_T89"],
"Position": variables["xGEO"],
"PSD": psd_var,
}
if saving_strategy is None:
save_srat_class = (
ep.saving_strategies.DailyLEORBStrategy if satellite == "S6-MF" else ep.saving_strategies.MonthlyRBStrategy
)
saving_strategy = save_srat_class(
base_data_path=Path(processed_data_path),
mission="ESA",
satellite=f"{satellite.lower()}",
instrument="ngrm",
mag_field="T89",
file_format=".nc",
data_standard=ep.data_standards.GFZStandard(),
)
ep.save(variables_to_save, saving_strategy, start_time, end_time, time_var=binned_time_var, append=True)
|