diff --git a/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml b/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml new file mode 100644 index 000000000..2b8ac177d --- /dev/null +++ b/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml @@ -0,0 +1,161 @@ +# Level 2 variable base dictionaries +model_base: &model_base + DEPEND_0: epoch + DISPLAY_TYPE: time_series + FILLVAL: -1.0e+31 + FORMAT: E12.2 + VALIDMIN: -1.0e+31 + VALIDMAX: -1.0e+31 + VAR_TYPE: data + SCALETYP: linear + +tof_model_base: &tof_model_base + <<: *model_base + DEPEND_1: mass_number + +model_amplitude_base: &model_amplitude_base + <<: *model_base + UNITS: dN + +model_time_base: &model_time_base + <<: *model_base + UNITS: s + +model_dimensionless_base: &model_dimensionless_base + <<: *model_base + UNITS: ' ' + +tof_model_amplitude_base: &tof_model_amplitude_base + <<: *tof_model_base + UNITS: dN + +tof_model_time_base: &tof_model_time_base + <<: *tof_model_base + UNITS: s + +tof_model_dimensionless_base: &tof_model_dimensionless_base + <<: *tof_model_base + UNITS: ' ' + +mass_number_attrs: + CATDESC: Mass number + DISPLAY_TYPE: no_plot + FIELDNAM: mass_number + FILLVAL: '' + FORMAT: E12.2 + LABLAXIS: mass_number + UNITS: '' + VALIDMIN: 1 + VALIDMAX: 50 + VAR_TYPE: support_data + VAR_NOTES: These represent the peaks of the TOF waveform + +target_high_model_amplitude: + <<: *model_amplitude_base + CATDESC: target_high_model_amplitude + FIELDNAM: target_high_model_amplitude + LABLAXIS: target_high_model_amplitude + VAR_NOTES: The amplitude of the response for target_high + +ion_grid_model_amplitude: + <<: *model_amplitude_base + CATDESC: ion_grid_model_amplitude + FIELDNAM: ion_grid_model_amplitude + LABLAXIS: ion_grid_model_amplitude + VAR_NOTES: The amplitude of the response for ion_grid + +target_high_model_uncertainty: + <<: *model_dimensionless_base + CATDESC: target_high_model_uncertainty + FIELDNAM: target_high_model_uncertainty + LABLAXIS: target_high_model_uncertainty + VAR_NOTES: The uncertainty in the model of the response for target_high + +ion_grid_model_uncertainty: + <<: *model_dimensionless_base + CATDESC: ion_grid_model_uncertainty + FIELDNAM: ion_grid_model_uncertainty + LABLAXIS: ion_grid_model_uncertainty + VAR_NOTES: The uncertainty in the model of the response for ion_grid + +target_high_model_constant_offset: + <<: *model_amplitude_base + CATDESC: target_high_model_constant_offset + FIELDNAM: target_high_model_constant_offset + VAR_NOTES: The constant offset of the response for target_high + +ion_grid_model_constant_offset: + <<: *model_amplitude_base + CATDESC: ion_grid_model_constant_offset + FIELDNAM: ion_grid_model_constant_offset + VAR_NOTES: The constant offset of the response for ion grid + +target_high_model_time_of_impact: + <<: *model_time_base + CATDESC: target_high_model_time_of_impact + FIELDNAM: target_high_model_time_of_impact + LABLAXIS: target_high_model_time_of_impact + VAR_NOTES: The time of impact for target_high + +ion_grid_model_time_of_impact: + <<: *model_time_base + CATDESC: ion_grid_model_time_of_impact + FIELDNAM: ion_grid_model_time_of_impact + LABLAXIS: ion_grid_model_time_of_impact + VAR_NOTES: The time of impact for ion_grid + +target_high_model_rise_time: + <<: *model_time_base + CATDESC: target_high_model_rise_time + FIELDNAM: target_high_model_rise_time + LABLAXIS: target_high_model_rise_time + VAR_NOTES: The rise time of the response for target_high + +ion_grid_model_rise_time: + <<: *model_time_base + CATDESC: ion_grid_model_rise_time + FIELDNAM: ion_grid_model_rise_time + LABLAXIS: ion_grid_model_rise_time + VAR_NOTES: The rise time of the response for ion_grid + +target_high_model_discharge_time: + <<: *model_time_base + CATDESC: target_high_model_discharge_time + FIELDNAM: target_high_model_discharge_time + LABLAXIS: target_high_model_discharge_time + VAR_NOTES: The discharge time of the response for target_high + +ion_grid_model_discharge_time: + <<: *model_time_base + CATDESC: ion_grid_model_discharge_time + FIELDNAM: ion_grid_model_discharge_time + LABLAXIS: ion_grid_model_discharge_time + VAR_NOTES: The discharge time of the response for ion_grid + +tof_low_model_masses_amplitude: + <<: *tof_model_amplitude_base + CATDESC: tof_low_model_masses_amplitude + FIELDNAM: tof_low_model_masses_amplitude + LABLAXIS: tof_low_model_masses_amplitude + VAR_NOTES: The amplitude of the first 50 peaks in tof_low + +tof_low_model_masses_center: + <<: *tof_model_amplitude_base + CATDESC: tof_low_model_masses_center + FIELDNAM: tof_low_model_masses_center + LABLAXIS: tof_low_model_masses_center + VAR_NOTES: The center of the first 50 peaks in tof_low + +tof_low_model_masses_sigma: + <<: *tof_model_dimensionless_base + CATDESC: tof_low_model_masses_sigma + FIELDNAM: tof_low_model_masses_sigma + LABLAXIS: tof_low_model_masses_sigma + VAR_NOTES: The sigma of the fitted exponentially modified gaussian to the first 50 peaks in tof_low + +tof_low_model_masses_gamma: + <<: *tof_model_dimensionless_base + CATDESC: tof_low_model_masses_gamma + FIELDNAM: tof_low_model_masses_gamma + LABLAXIS: tof_low_model_masses_gamma + VAR_NOTES: The gamma of the fitted exponentially modified gaussian to the first 50 peaks in tof_low \ No newline at end of file diff --git a/imap_processing/idex/constants.py b/imap_processing/idex/constants.py new file mode 100644 index 000000000..475bc1f4b --- /dev/null +++ b/imap_processing/idex/constants.py @@ -0,0 +1,10 @@ +"""Contains dataclasses to support IDEX processing.""" + +TARGET_HIGH_CALIBRATION = 0.00135597 +ION_GRID_CALIBRATION = 0.00026148 +time_of_impact_init = 20 +constant_offset_init = 0.0 +rise_time_init = 3.71 +discharge_time_init = 37.1 +FILLVAL = -1.0e31 +TOF_Low_Peak_Prominence = 7 diff --git a/imap_processing/idex/l2/__init__.py b/imap_processing/idex/l2/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/imap_processing/idex/l2/idex_l2.py b/imap_processing/idex/l2/idex_l2.py new file mode 100644 index 000000000..443bbc072 --- /dev/null +++ b/imap_processing/idex/l2/idex_l2.py @@ -0,0 +1,568 @@ +""" +Perform IDEX l2 Processing. + +TODO Finish Docstring. +""" + +# ruff: noqa: PLR0913 + +import logging +from pathlib import Path + +import lmfit +import numpy as np +import xarray as xr + +# from cdflib.xarray import xarray_to_cdf +from lmfit.model import ModelResult +from scipy.optimize import curve_fit +from scipy.signal import butter, filtfilt, find_peaks +from scipy.special import erfc + +from imap_processing.cdf.imap_cdf_manager import ImapCdfAttributes +from imap_processing.cdf.utils import load_cdf, write_cdf +from imap_processing.idex import constants + + +def get_idex_attrs(data_version: str) -> ImapCdfAttributes: + """ + Load in CDF attributes for IDEX l2 files. + + Parameters + ---------- + data_version : str + Data version for CDF filename, in the format "vXXX". + + Returns + ------- + idex_attrs : ImapCdfAttributes + Imap object with l1a attribute files loaded in. + """ + idex_attrs = ImapCdfAttributes() + idex_attrs.add_instrument_global_attrs("idex") + idex_attrs.add_instrument_variable_attrs("idex", "l2") + idex_attrs.add_global_attribute("Data_version", data_version) + return idex_attrs + + +class L2Processor: + """ + L2 processing class. + + Parameters + ---------- + l1_file : str + File path to l1 file being processed to l2 data. + data_version : str + The version of the data product being created. + """ + + def __init__(self, l1_file: str, data_version: str): + """ + Function/method description. + + Parameters + ---------- + l1_file : str + File path to l1 file being processed to l2 data. + data_version : str + The version of the data product being created. + """ + self.l1_file = l1_file + + # Switched from cdf_to_xarray to load_cdf function + # Now l1 data is stored in a xarray. + self.l1_data = load_cdf(Path(l1_file)) + + # TODO: Perhaps I want to make this just code inside the + # init rather than a function? + self.l2_attrs = get_idex_attrs(data_version) + + target_signal_model_dataset = self.model_fitter( + "Target_High", constants.TARGET_HIGH_CALIBRATION, butterworth_filter=False + ) + ion_grid_model_dataset = self.model_fitter( + "Ion_Grid", constants.TARGET_HIGH_CALIBRATION, butterworth_filter=True + ) + tof_model_dataset = self.fit_tof_model( + "TOF_Low", peak_prominence=constants.TOF_Low_Peak_Prominence + ) + + self.l2_data = xr.merge( + [ + self.l1_data, + target_signal_model_dataset, + ion_grid_model_dataset, + tof_model_dataset, + ] + ) + + def model_fitter( + self, + variable: str, + amplitude_calibration: float, + butterworth_filter: bool = False, + ) -> xr: + """ + Function/method description. + + Parameters + ---------- + variable : str + Something. + + amplitude_calibration : float + Something. + + butterworth_filter : bool + Something. + + Returns + ------- + xr.concat : xarray + Something. + """ + model_fit_list = [] + for impact in self.l1_data[variable]: + epoch_xr = xr.DataArray( + name="epoch", + # TODO: What should the impact time be? in RawDustEvents + data=[impact["epoch"].data], + dims="epoch", + # TODO: Double check that this is correct + attrs=self.l2_attrs.get_variable_attributes("epoch"), + ) + + x = impact[impact.attrs["DEPEND_1"]].data + y = impact.data - np.mean(impact.data[0:10]) + try: + model = lmfit.Model(self.idex_response_function) + params = model.make_params( + time_of_impact=constants.time_of_impact_init, + constant_offset=constants.constant_offset_init, + amplitude=max(y), + rise_rime=constants.rise_time_init, + discharge_time=constants.discharge_time_init, + ) + params["rise_time"].min = 5 + params["rise_time"].max = 10000 + + if butterworth_filter: + y = self.butter_lowpass_filter(y, x) + + result = model.fit(y, params, x=x) + + param = result.best_values + + _, param_cov, _, _, _ = curve_fit(self.idex_response_function, x, y) + fit_uncertainty = np.linalg.det(param_cov) + + time_of_impact_fit = param["time_of_impact"] + constant_offset_fit = param["constant_offset"] + amplitude_fit = amplitude_calibration * param["amplitude"] + rise_time_fit = param["rise_time"] + discharge_time_fit = param["discharge_time"] + + except Exception as e: + logging.warning( + "Error fitting Models, resorting to FILLVALs: " + str(e) + ) + time_of_impact_fit = constants.FILLVAL + constant_offset_fit = constants.FILLVAL + amplitude_fit = constants.FILLVAL + rise_time_fit = constants.FILLVAL + discharge_time_fit = constants.FILLVAL + fit_uncertainty = constants.FILLVAL + + variable_lower = variable.lower() + + time_of_impact_fit_xr = xr.DataArray( + # Target_High_model_time_of_impact + # Ion_Grid_model_time_of_impact + name=f"{variable}_model_time_of_impact", + data=[time_of_impact_fit], + dims="epoch", + # TODO: attrs + attrs=self.l2_attrs.get_variable_attributes( + f"{variable_lower}_model_time_of_impact" + ), + ) + + constant_offset_fit_xr = xr.DataArray( + # Target_High_model_constant_offset + # Ion_Grid_model_time_of_impact + name=f"{variable}_model_constant_offset", + data=[constant_offset_fit], + dims="epoch", + # TODO: attrs + attrs=self.l2_attrs.get_variable_attributes( + f"{variable_lower}_model_constant_offset" + ), + ) + + amplitude_fit_xr = xr.DataArray( + # Target_High_model_amplitude + # Ion_Grid_model_amplitude + name=f"{variable}_model_amplitude", + data=[amplitude_fit], + dims="epoch", + # TODO: attrs + attrs=self.l2_attrs.get_variable_attributes( + f"{variable_lower}_model_amplitude" + ), + ) + + rise_time_fit_xr = xr.DataArray( + # Target_High_model_rise_time + # Ion_Grid_model_rise_time + name=f"{variable}_model_rise_time", + data=[rise_time_fit], + dims="epoch", + # TODO: attrs + attrs=self.l2_attrs.get_variable_attributes( + f"{variable_lower}_model_rise_time" + ), + ) + + discharge_time_xr = xr.DataArray( + # Target_High_model_discharge_time + # Ion_Grid_model_discharge_time + name=f"{variable}_model_discharge_time", + data=[discharge_time_fit], + dims="epoch", + # TODO: attrs + attrs=self.l2_attrs.get_variable_attributes( + f"{variable_lower}_model_discharge_time" + ), + ) + + fit_uncertainty_xr = xr.DataArray( + # Target_High_model_uncertainty + # Ion_Grid_model_uncertainty + name=f"{variable}_model_uncertainty", + data=[fit_uncertainty], + dims="epoch", + # TODO: attrs + attrs=self.l2_attrs.get_variable_attributes( + f"{variable_lower}_model_uncertainty" + ), + ) + + model_fit_list.append( + xr.Dataset( + data_vars={ + f"{variable}_model_time_Of_impact": time_of_impact_fit_xr, + f"{variable}_model_constant_offset": constant_offset_fit_xr, + f"{variable}_model_amplitude": amplitude_fit_xr, + f"{variable}_model_rise_time": rise_time_fit_xr, + f"{variable}_model_discharge_time": discharge_time_xr, + f"{variable}_model_uncertainty": fit_uncertainty_xr, + }, + coords={"epoch": epoch_xr}, + ) + ) + + return xr.concat(model_fit_list, dim="epoch") + + @staticmethod + def idex_response_function( + x: int, + time_of_impact: int, + constant_offset: int, + amplitude: int, + rise_time: int, + discharge_time: int, + ) -> float: + """ + Function/method description. + + Parameters + ---------- + x : int + Something. + + time_of_impact : int + Something. + + constant_offset : int + Something. + + amplitude : int + Something. + + rise_time : int + Something. + + discharge_time : int + Something. + + Returns + ------- + result : Union[int, float] + Something. + """ + heaviside = np.heaviside(x - time_of_impact, 0) + exponent_1 = 1.0 - np.exp(-(x - time_of_impact) / rise_time) + exponent_2 = np.exp(-(x - time_of_impact) / discharge_time) + result: float = constant_offset + ( + heaviside * amplitude * exponent_1 * exponent_2 + ) + return result + + # fmt: skip + + # Create a model for exponentially modified Gaussian + @staticmethod + def expgaussian( + x: int, amplitude: int, center: int, sigma: int, gamma: int + ) -> float: + """ + Function/method description. + + Parameters + ---------- + x : int + Something. + + amplitude : int + Something. + + center : int + Something. + + sigma : int + Something. + + gamma : int + Something. + + Returns + ------- + result : float + Something. + """ + dx = center - x + + result: float = amplitude * np.exp(gamma * dx) * erfc(dx / (np.sqrt(2) * sigma)) + return result + + @staticmethod + def butter_lowpass_filter(data: str, time: list) -> np.ndarray: + """ + Function/method description. + + Parameters + ---------- + data : str + Something. + + time : list + Something. + + Returns + ------- + y : np.ndarray + Something. + """ + # Filter requirements. + t = time[1] - time[0] # |\Sample Period (s) + fs = (time[-1] - time[0]) / t # ||sample rate, Hz + cutoff = 10 # ||desired cutoff frequency of the filter, Hz + nyq = 0.5 * fs # ||Nyquist Frequency + order = 2 # ||sine wave can be approx represented as quadratic + normal_cutoff = cutoff / nyq + # Get the filter coefficients + b, a, _ = butter(order, normal_cutoff, btype="low", analog=False) + y: np.ndarray = filtfilt(b, a, data) + return y + + def fit_tof_model(self, variable: str, peak_prominence: int) -> xr: + """ + Function/method description. + + Parameters + ---------- + variable : str + Something. + + peak_prominence : int + Something. + + Returns + ------- + xr.concat : xarray + Something. + """ + mass_number_xr = xr.DataArray( + name="mass_number", + data=np.linspace(1, 50, 50), + dims="mass_number", + # TODO: check this is correct + attrs=self.l2_attrs.get_variable_attributes("mass_number_attrs"), + ) + + tof_model_parameters_list = [] + for impact in self.l1_data[variable]: + epoch_xr = xr.DataArray( + name="epoch", + # TODO: What should the impact time be? in RawDustEvents + data=[impact["epoch"].data], + dims="epoch", + attrs=self.l2_attrs.get_variable_attributes("epoch"), + ) + + mass_amplitudes = np.full(50, constants.FILLVAL) + mass_centers = np.full(50, constants.FILLVAL) + mass_sigmas = np.full(50, constants.FILLVAL) + mass_gammas = np.full(50, constants.FILLVAL) + + x = impact[impact.attrs["DEPEND_1"]].data + y = impact.data + + peaks, _ = find_peaks(y, prominence=peak_prominence) + i = 0 + for peak in peaks: + try: + i += 1 + fit_params = self.fit_expgaussian( + x[peak - 10 : peak + 10], y[peak - 10 : peak + 10] + ).best_values + ( + mass_amplitudes[i], + mass_centers[i], + mass_sigmas[i], + mass_gammas[i], + ) = tuple(fit_params.values()) + except Exception as e: + logging.warning( + "Error fitting TOF Model. Defaulting to FILLVALS. " + str(e) + ) + + variable_lower = variable.lower() + + amplitude_xr = xr.DataArray( + # TOF_Low_model_masses_amplitude + name=f"{variable}_model_masses_amplitude", + data=[mass_amplitudes], + dims=("epoch", "mass_number"), + # TODO: Attrs + attrs=self.l2_attrs.get_variable_attributes( + f"{variable_lower}_model_masses_amplitude" + ), + ) + + center_xr = xr.DataArray( + # TOF_Low_model_masses_center + name=f"{variable}_model_masses_center", + data=[mass_centers], + dims=("epoch", "mass_number"), + # TODO: Attrs + attrs=self.l2_attrs.get_variable_attributes( + f"{variable_lower}_model_masses_center" + ), + ) + + sigma_xr = xr.DataArray( + # TOF_Low_model_masses_sigma + name=f"{variable}_model_masses_sigma", + data=[mass_sigmas], + dims=("epoch", "mass_number"), + # TODO: Attrs + attrs=self.l2_attrs.get_variable_attributes( + f"{variable_lower}_model_masses_sigma" + ), + ) + + gamma_xr = xr.DataArray( + # TOF_Low_model_masses_gamma + name=f"{variable}_model_masses_gamma", + data=[mass_gammas], + dims=("epoch", "mass_number"), + # TODO: Attrs + attrs=self.l2_attrs.get_variable_attributes( + f"{variable_lower}_model_masses_gamma" + ), + ) + + tof_model_parameters_list.append( + xr.Dataset( + data_vars={ + f"{variable}_model_masses_amplitude": amplitude_xr, + f"{variable}_model_masses_center": center_xr, + f"{variable}_model_masses_sigma": sigma_xr, + f"{variable}_model_Mmsses_gamma": gamma_xr, + }, + coords={"epoch": epoch_xr, "mass_number": mass_number_xr}, + ) + ) + + return xr.concat(tof_model_parameters_list, dim="epoch") + + # Fit the exponentially modified Gaussian + def fit_expgaussian(self, x: str, y: str) -> ModelResult: + """ + Function/method description. + + Parameters + ---------- + x : str + Something. + + y : str + Something. + + Returns + ------- + result.best_value : dict + Something. + """ + model = lmfit.Model(self.expgaussian) + params = model.make_params( + amplitude=max(y), center=x[np.argmax(y)], sigma=10.0, gamma=10.0 + ) + result = model.fit(y, params, x=x) + return result + + # def write_l2_cdf(self) -> Path: + # """ + # Function/method description. + # + # Returns + # ------- + # l2_file_name : Path + # The file name of the l2 file. + # """ + # + # return write_cdf(self.l2_data) + + def process_idex_l2(self, l1_file: str, data_version: str) -> str: + """ + Function/method description. + + Parameters + ---------- + l1_file : str + Something. + + data_version : str + Something. + + Returns + ------- + l2_cdf_file_name : str + The file name of the l2 cdf. + + Notes + ----- + Example usage -> + from imap_processing.idex.idex_packet_parser import PacketParser + from imap_processing.idex.l2_processing import L2Processor + l0_file = "imap_processing/idex/tests/imap_idex_l0_20230725_v01-00.pkts" + l1_data = PacketParser(l0_file) + l1_data.write_cdf_file("20230725") + l2_data = L2Processor('imap_idex_l1_20230725_v01.cdf') + l2_data.write_l2_cdf() + """ + l2_data = L2Processor(l1_file, data_version) + + return str(write_cdf(l2_data.l2_data)) diff --git a/imap_processing/tests/idex/test_idex_l2.py b/imap_processing/tests/idex/test_idex_l2.py new file mode 100644 index 000000000..4717fbe76 --- /dev/null +++ b/imap_processing/tests/idex/test_idex_l2.py @@ -0,0 +1,35 @@ +from pathlib import Path + +import pytest + +from imap_processing import imap_module_directory +from imap_processing.cdf.utils import load_cdf, write_cdf +from imap_processing.idex.l1.idex_l1 import PacketParser +from imap_processing.idex.l2.idex_l2 import L2Processor + +# from imap_processing.idex.l2.idex_l2 import read_and_return + + +@pytest.fixture() +def decom_test_data(): + test_file = Path( + f"{imap_module_directory}/tests/idex/imap_idex_l0_raw_20230725_v001.pkts" + ) + return PacketParser(test_file, "v001").data + + +@pytest.fixture() +def l1_cdf(decom_test_data): + """ + from imap_processing.idex.idex_packet_parser import PacketParser + l0_file = "imap_processing/tests/idex/imap_idex_l0_sci_20230725_v001.pkts" + l1_data = PacketParser(l0_file, data_version) + l1_data.write_l1_cdf() + """ + + return write_cdf(decom_test_data) + + +def test_l1_xarray(l1_cdf): + l1_data = L2Processor(l1_cdf, "v001").l1_data + assert l1_data == load_cdf(l1_cdf) diff --git a/poetry.lock b/poetry.lock index 83c5479d1..3e0fd037c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -29,34 +29,51 @@ files = [ {file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"}, ] +[[package]] +name = "asteval" +version = "1.0.2" +description = "Safe, minimalistic evaluator of python expression using ast module" +optional = false +python-versions = ">=3.8" +files = [ + {file = "asteval-1.0.2-py3-none-any.whl", hash = "sha256:2ece3179809185598d054289e089b8e7706c1a5afa5f7e7c236b3cdae0cf8290"}, + {file = "asteval-1.0.2.tar.gz", hash = "sha256:00e27d9dc565858056d04ebf741f9db4dc4ba319ac3517d094b1608e7f73fa6a"}, +] + +[package.extras] +all = ["asteval[dev,doc,test]"] +dev = ["build", 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