From c82651e0b0621f7830fb27ba21c6853994817575 Mon Sep 17 00:00:00 2001
From: Ana Manica <ana@manica.org>
Date: Fri, 9 Aug 2024 13:58:16 -0600
Subject: [PATCH 1/7] Initial commit

---
 .../config/imap_idex_l2_variable_attrs.yaml   |   0
 imap_processing/idex/constants.py             |  10 +
 imap_processing/idex/l2/__init__.py           |   0
 imap_processing/idex/l2/idex_l2.py            | 542 ++++++++++++++++++
 imap_processing/tests/idex/test_idex_l2.py    |  34 ++
 5 files changed, 586 insertions(+)
 create mode 100644 imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml
 create mode 100644 imap_processing/idex/constants.py
 create mode 100644 imap_processing/idex/l2/__init__.py
 create mode 100644 imap_processing/idex/l2/idex_l2.py
 create mode 100644 imap_processing/tests/idex/test_idex_l2.py

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..e69de29bb
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..1794be754
--- /dev/null
+++ b/imap_processing/idex/l2/idex_l2.py
@@ -0,0 +1,542 @@
+"""
+Perform IDEX l2 Processing.
+
+This module processes decommutated IDEX packets and creates l2 data products.
+"""
+
+import logging
+from pathlib import Path
+
+import lmfit
+import numpy as np
+import xarray as xr
+from cdflib.xarray import xarray_to_cdf
+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
+from imap_processing.idex import constants
+
+# from . import idex_cdf_attrs
+
+
+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:
+    """
+    Example
+    -------
+    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()
+    """
+
+    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
+        self.l1_data = load_cdf(Path(l1_file))
+
+        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,
+            ]
+        )
+
+        # TODO: Perhaps I want to make this just code inside the
+        #  init rather than a function?
+        self.l2_attrs = get_idex_attrs(data_version)
+
+    def model_fitter(
+        self,
+        variable: str,
+        amplitude_calibration: float,
+        butterworth_filter: bool = False,
+    ):
+        """
+        Function/method description.
+
+        Parameters
+        ----------
+        variable: str
+            Something
+
+        amplitude_calibration: float
+            Something
+
+        butterworth_filter: bool
+            Something
+
+        Returns
+        -------
+        xr.concat
+            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"),
+                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
+
+            time_of_impact_fit_xr = xr.DataArray(
+                name=f"{variable}_Model_Time_Of_Impact",
+                data=[time_of_impact_fit],
+                dims=("epoch"),
+                # TODO: attrs
+            )
+
+            constant_offset_fit_xr = xr.DataArray(
+                name=f"{variable}_Model_Constant_Offset",
+                data=[constant_offset_fit],
+                dims=("epoch"),
+                # TODO: attrs
+            )
+
+            amplitude_fit_xr = xr.DataArray(
+                name=f"{variable}_Model_Amplitude",
+                data=[amplitude_fit],
+                dims=("epoch"),
+                # TODO: attrs
+            )
+
+            rise_time_fit_xr = xr.DataArray(
+                name=f"{variable}_Model_Rise_time",
+                data=[rise_time_fit],
+                dims=("epoch"),
+                # TODO: attrs
+            )
+
+            discharge_time_xr = xr.DataArray(
+                name=f"{variable}_Model_Discharge_time",
+                data=[discharge_time_fit],
+                dims=("epoch"),
+                # TODO: attrs
+            )
+
+            fit_uncertainty_xr = xr.DataArray(
+                name=f"{variable}_Model_Uncertainty",
+                data=[fit_uncertainty],
+                dims=("epoch"),
+                # TODO: attrs
+            )
+
+            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, time_of_impact, constant_offset, amplitude, rise_time, discharge_time
+    ):
+        """
+        Docstring.
+        """
+        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)
+        return constant_offset + (heaviside * amplitude * exponent_1 * exponent_2)
+
+    # fmt: skip
+
+    # Create a model for exponentially modified Gaussian
+    @staticmethod
+    def expgaussian(x, amplitude, center, sigma, gamma):
+        """
+        Docstring.
+        """
+        dx = center - x
+        return amplitude * np.exp(gamma * dx) * erfc(dx / (np.sqrt(2) * sigma))
+
+    @staticmethod
+    def butter_lowpass_filter(data, time):
+        """
+        Docstring.
+        """
+        # 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 = filtfilt(b, a, data)
+        return y
+
+    def fit_tof_model(self, variable, peak_prominence):
+        """
+        Function/method description.
+
+        Parameters
+        ----------
+        variable: str
+            Something
+
+        peak_prominence
+            Something
+        """
+        mass_number_xr = xr.DataArray(
+            name="mass_number",
+            data=np.linspace(1, 50, 50),
+            dims=("mass_number"),
+            # TODO: Attrs
+            # 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]
+                    )
+                    (
+                        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)
+                    )
+
+            amplitude_xr = xr.DataArray(
+                name=f"{variable}_model_masses_amplitude",
+                data=[mass_amplitudes],
+                dims=("epoch", "mass_number"),
+                # TODO: Attrs
+            )
+
+            center_xr = xr.DataArray(
+                name=f"{variable}_model_masses_center",
+                data=[mass_centers],
+                dims=("epoch", "mass_number"),
+                # TODO: Attrs
+            )
+
+            sigma_xr = xr.DataArray(
+                name=f"{variable}_model_masses_sigma",
+                data=[mass_sigmas],
+                dims=("epoch", "mass_number"),
+                # TODO: Attrs
+            )
+
+            gamma_xr = xr.DataArray(
+                name=f"{variable}_model_masses_gamma",
+                data=[mass_gammas],
+                dims=("epoch", "mass_number"),
+                # TODO: Attrs
+            )
+
+            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, y):
+        """
+        Function/method description.
+        """
+        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.best_values
+
+    def write_l2_cdf(self):
+        """
+        Function/method description.
+        """
+        # TODO: Do I need a get_global_attributes line here?
+        # self.l2_data.attrs = idex_cdf_attrs.idex_l2_global_attrs
+
+        for var in self.l2_data:
+            if "_model_amplitude" in var:
+                self.l2_data[var].attrs = {
+                    "CATDESC": var,
+                    "FIELDNAM": var,
+                    "LABLAXIS": var,
+                    "VAR_NOTES": f"The amplitude of the response for "
+                    f"{var.replace('_model_amplitude', '')}",
+                }
+                # | idex_cdf_attrs.model_amplitude_base
+                # self.l2_attrs.get_variable_attributes("model_amplitude_base")
+
+            if "_model_uncertainty" in var:
+                self.l2_data[var].attrs = {
+                    "CATDESC": var,
+                    "FIELDNAM": var,
+                    "LABLAXIS": var,
+                    "VAR_NOTES": f"The uncertainty in the model of the response for "
+                    f"{var.replace('_model_amplitude', '')}",
+                }
+                # | idex_cdf_attrs.model_dimensionless_base
+                # self.l2_attrs.get_variable_attributes("model_dimensionless_base")
+
+            if "_model_constant_offset" in var:
+                self.l2_data[var].attrs = {
+                    "CATDESC": var,
+                    "FIELDNAM": var,
+                    "VAR_NOTES": f"The constant offset of the response for "
+                    f"{var.replace('_model_constant_offset', '')}",
+                }
+                # | idex_cdf_attrs.model_amplitude_base
+                # self.l2_attrs.get_variable_attributes("model_amplitude_base")
+
+            if "_model_time_of_impact" in var:
+                self.l2_data[var].attrs = {
+                    "CATDESC": var,
+                    "FIELDNAM": var,
+                    "LABLAXIS": var,
+                    "VAR_NOTES": f"The time of impact for "
+                    f"{var.replace('_model_time_of_impact', '')}",
+                }
+                # | idex_cdf_attrs.model_time_base
+                # self.l2_attrs.get_variable_attributes("model_time_base")
+
+            if "_model_rise_time" in var:
+                self.l2_data[var].attrs = {
+                    "CATDESC": var,
+                    "FIELDNAM": var,
+                    "LABLAXIS": var,
+                    "VAR_NOTES": f"The rise time of the response for "
+                    f"{var.replace('_model_rise_time', '')}",
+                }
+                # | idex_cdf_attrs.model_time_base
+                # self.l2_attrs.get_variable_attributes("model_time_base")
+
+            if "_model_discharge_time" in var:
+                self.l2_data[var].attrs = {
+                    "CATDESC": var,
+                    "FIELDNAM": var,
+                    "LABLAXIS": var,
+                    "VAR_NOTES": f"The discharge time of the response for "
+                    f"{var.replace('_model_discharge_time', '')}",
+                }
+                # | idex_cdf_attrs.model_time_base
+                # self.l2_attrs.get_variable_attributes("model_time_base")
+            if "_model_masses_amplitude" in var:
+                self.l2_data[var].attrs = {
+                    "CATDESC": var,
+                    "FIELDNAM": var,
+                    "LABLAXIS": var,
+                    "VAR_NOTES": f"The amplitude of the first 50 peaks in "
+                    f"{var.replace('_Model_Masses_Amplitude', '')}",
+                }
+                # | idex_cdf_attrs.tof_model_amplitude_base
+                # self.l2_attrs.get_variable_attributes("tof_model_amplitude_base")
+
+            if "_model_masses_center" in var:
+                self.l2_data[var].attrs = {
+                    "CATDESC": var,
+                    "FIELDNAM": var,
+                    "LABLAXIS": var,
+                    "VAR_NOTES": f"The center of the first 50 peaks in "
+                    f"{var.replace('_model_masses_center', '')}",
+                }
+                # | idex_cdf_attrs.tof_model_dimensionless_base
+                # self.l2_attrs.get_variable_attributes("tof_model_amplitude_base")
+
+            if "_model_masses_sigma" in var:
+                self.l2_data[var].attrs = {
+                    "CATDESC": var,
+                    "FIELDNAM": var,
+                    "LABLAXIS": var,
+                    "VAR_NOTES": f"The sigma of the fitted exponentially modified "
+                    f"gaussian to the first 50 peaks in {var.replace('_model_masses_sigma', '')}",
+                }
+                # | idex_cdf_attrs.tof_model_dimensionless_base
+                # self.l2_attrs.get_variable_attributes("tof_model_dimensionless_base")
+
+            if "_model_masses_gamma" in var:
+                self.l2_data[var].attrs = {
+                    "CATDESC": var,
+                    "FIELDNAM": var,
+                    "LABLAXIS": var,
+                    "VAR_NOTES": f"The gamma of the fitted exponentially modified "
+                    f"gaussian to the first 50 peaks in {var.replace('_model_masses_gamma', '')}",
+                }
+                # | idex_cdf_attrs.tof_model_dimensionless_base
+                # self.l2_attrs.get_variable_attributes("tof_model_dimensionless_base")
+
+        l2_file_name = self.l1_file.replace("_l1_", "_l2_")
+
+        xarray_to_cdf(self.l2_data, l2_file_name)
+
+        return l2_file_name
+
+    def process_idex_l2(self, l1_file: str, data_version: str):
+        """
+        Function/method description.
+
+        Parameters
+        ----------
+        l1_file: str
+            Something
+
+        data_version: str
+            Something
+
+        Returns
+        -------
+        l2_cdf_file_name
+
+        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)
+
+        l2_cdf_file_name = l2_data.write_l2_cdf()
+
+        return l2_cdf_file_name
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..4b7150e34
--- /dev/null
+++ b/imap_processing/tests/idex/test_idex_l2.py
@@ -0,0 +1,34 @@
+from pathlib import Path
+
+import pytest
+
+from imap_processing import imap_module_directory
+from imap_processing.cdf.utils import write_cdf
+from imap_processing.idex.l1.idex_l1 import PacketParser
+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_read_and_return(l1_cdf):
+    dataset = read_and_return(l1_cdf)
+
+    assert dataset == decom_test_data

From b2689e3b4228302ad655a0af840f7fd801effda6 Mon Sep 17 00:00:00 2001
From: Ana Manica <ana@manica.org>
Date: Thu, 15 Aug 2024 10:51:30 -0600
Subject: [PATCH 2/7] CDf Attrs l2 started

---
 .../config/imap_idex_l2_variable_attrs.yaml   | 149 ++++++++++
 imap_processing/idex/l2/idex_l2.py            | 261 +++++++++++++-----
 imap_processing/tests/idex/test_idex_l2.py    |  13 +-
 3 files changed, 343 insertions(+), 80 deletions(-)

diff --git a/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml b/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml
index e69de29bb..8617f78b7 100644
--- a/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml
+++ b/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml
@@ -0,0 +1,149 @@
+# Level 2 variable base dictionaries
+model_base: &model_base
+  DEPEND_0: epoch
+  DISPLAY_TYPE: time_series
+  FILLVAL: np.array([-1.e+31], dtype=np.float32),
+  FORMAT: E12.2
+  # TODO: make these not...
+  VALIDMIN: np.array([-1.e+31], dtype=np.float32),
+  VALIDMAX: np.array([1.e+31], dtype=np.float32),
+  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_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: ion_grid_model_discharge_time
+  FIELDNAM: ion_grid_model_discharge_time
+  LABLAXIS: ion_grid_model_discharge_time
+  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/l2/idex_l2.py b/imap_processing/idex/l2/idex_l2.py
index 1794be754..f88ffe0fd 100644
--- a/imap_processing/idex/l2/idex_l2.py
+++ b/imap_processing/idex/l2/idex_l2.py
@@ -1,9 +1,11 @@
 """
 Perform IDEX l2 Processing.
 
-This module processes decommutated IDEX packets and creates l2 data products.
+TODO Finish Docstring.
 """
 
+# ruff: noqa: PLR0913
+
 import logging
 from pathlib import Path
 
@@ -11,6 +13,7 @@
 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
@@ -19,8 +22,6 @@
 from imap_processing.cdf.utils import load_cdf
 from imap_processing.idex import constants
 
-# from . import idex_cdf_attrs
-
 
 def get_idex_attrs(data_version: str) -> ImapCdfAttributes:
     """
@@ -45,17 +46,14 @@ def get_idex_attrs(data_version: str) -> ImapCdfAttributes:
 
 class L2Processor:
     """
-    Example
-    -------
-    from imap_processing.idex.idex_packet_parser import PacketParser
-    from imap_processing.idex.l2_processing import L2Processor
+    L2 processing class.
 
-    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()
+    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):
@@ -64,16 +62,23 @@ def __init__(self, l1_file: str, data_version: str):
 
         Parameters
         ----------
-        l1_file: str
-            File path to l1 file being processed to l2 data
+        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))
 
+        print(self.l1_data["Target_High"])
+
+        # 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
         )
@@ -93,35 +98,30 @@ def __init__(self, l1_file: str, data_version: str):
             ]
         )
 
-        # TODO: Perhaps I want to make this just code inside the
-        #  init rather than a function?
-        self.l2_attrs = get_idex_attrs(data_version)
-
     def model_fitter(
         self,
         variable: str,
         amplitude_calibration: float,
         butterworth_filter: bool = False,
-    ):
+    ) -> xr:
         """
         Function/method description.
 
         Parameters
         ----------
-        variable: str
-            Something
+        variable : str
+            Something.
 
-        amplitude_calibration: float
-            Something
+        amplitude_calibration : float
+            Something.
 
-        butterworth_filter: bool
-            Something
+        butterworth_filter : bool
+            Something.
 
         Returns
         -------
-        xr.concat
-            Something
-
+        xr.concat : xarray
+            Something.
         """
         model_fit_list = []
         for impact in self.l1_data[variable]:
@@ -130,6 +130,7 @@ def model_fitter(
                 # 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"),
             )
 
@@ -175,42 +176,54 @@ def model_fitter(
                 fit_uncertainty = constants.FILLVAL
 
             time_of_impact_fit_xr = xr.DataArray(
-                name=f"{variable}_Model_Time_Of_Impact",
+                # 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
             )
 
             constant_offset_fit_xr = xr.DataArray(
-                name=f"{variable}_Model_Constant_Offset",
+                # 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
             )
 
             amplitude_fit_xr = xr.DataArray(
-                name=f"{variable}_Model_Amplitude",
+                # Target_High_model_amplitude
+                # Ion_Grid_model_amplitude
+                name=f"{variable}_model_amplitude",
                 data=[amplitude_fit],
                 dims=("epoch"),
                 # TODO: attrs
             )
 
             rise_time_fit_xr = xr.DataArray(
-                name=f"{variable}_Model_Rise_time",
+                # Target_High_model_rise_time
+                # Ion_Grid_model_rise_time
+                name=f"{variable}_model_rise_time",
                 data=[rise_time_fit],
                 dims=("epoch"),
                 # TODO: attrs
             )
 
             discharge_time_xr = xr.DataArray(
-                name=f"{variable}_Model_Discharge_time",
+                # Target_High_model_discharge_time
+                # Ion_Grid_model_discharge_time
+                name=f"{variable}_model_discharge_time",
                 data=[discharge_time_fit],
                 dims=("epoch"),
                 # TODO: attrs
             )
 
             fit_uncertainty_xr = xr.DataArray(
-                name=f"{variable}_Model_Uncertainty",
+                # Target_High_model_uncertainty
+                # Ion_Grid_model_uncertainty
+                name=f"{variable}_model_uncertainty",
                 data=[fit_uncertainty],
                 dims=("epoch"),
                 # TODO: attrs
@@ -219,12 +232,12 @@ def model_fitter(
             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,
+                        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},
                 )
@@ -234,31 +247,103 @@ def model_fitter(
 
     @staticmethod
     def idex_response_function(
-        x, time_of_impact, constant_offset, amplitude, rise_time, discharge_time
-    ):
+        x: int,
+        time_of_impact: int,
+        constant_offset: int,
+        amplitude: int,
+        rise_time: int,
+        discharge_time: int,
+    ) -> float:
         """
-        Docstring.
+        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)
-        return constant_offset + (heaviside * amplitude * exponent_1 * exponent_2)
+        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, amplitude, center, sigma, gamma):
+    def expgaussian(
+        x: int, amplitude: int, center: int, sigma: int, gamma: int
+    ) -> float:
         """
-        Docstring.
+        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
-        return amplitude * np.exp(gamma * dx) * erfc(dx / (np.sqrt(2) * sigma))
+
+        result: float = amplitude * np.exp(gamma * dx) * erfc(dx / (np.sqrt(2) * sigma))
+        return result
 
     @staticmethod
-    def butter_lowpass_filter(data, time):
+    def butter_lowpass_filter(data: str, time: list) -> np.ndarray:
         """
-        Docstring.
+        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)
@@ -269,27 +354,32 @@ def butter_lowpass_filter(data, time):
         normal_cutoff = cutoff / nyq
         # Get the filter coefficients
         b, a, _ = butter(order, normal_cutoff, btype="low", analog=False)
-        y = filtfilt(b, a, data)
+        y: np.ndarray = filtfilt(b, a, data)
         return y
 
-    def fit_tof_model(self, variable, peak_prominence):
+    def fit_tof_model(self, variable: str, peak_prominence: int) -> xr:
         """
         Function/method description.
 
         Parameters
         ----------
-        variable: str
-            Something
+        variable : str
+            Something.
+
+        peak_prominence : int
+            Something.
 
-        peak_prominence
-            Something
+        Returns
+        -------
+        xr.concat : xarray
+            Something.
         """
         mass_number_xr = xr.DataArray(
             name="mass_number",
             data=np.linspace(1, 50, 50),
             dims=("mass_number"),
-            # TODO: Attrs
-            # attrs = self.l2_attrs.get_variable_attributes((mass_number_attrs))
+            # TODO: check this is correct
+            attrs=self.l2_attrs.get_variable_attributes("mass_number_attrs"),
         )
 
         tof_model_parameters_list = []
@@ -317,7 +407,7 @@ def fit_tof_model(self, variable, peak_prominence):
                     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],
@@ -330,6 +420,7 @@ def fit_tof_model(self, variable, peak_prominence):
                     )
 
             amplitude_xr = xr.DataArray(
+                # TOF_Low_model_masses_amplitude
                 name=f"{variable}_model_masses_amplitude",
                 data=[mass_amplitudes],
                 dims=("epoch", "mass_number"),
@@ -337,6 +428,7 @@ def fit_tof_model(self, variable, peak_prominence):
             )
 
             center_xr = xr.DataArray(
+                # TOF_Low_model_masses_center
                 name=f"{variable}_model_masses_center",
                 data=[mass_centers],
                 dims=("epoch", "mass_number"),
@@ -344,6 +436,7 @@ def fit_tof_model(self, variable, peak_prominence):
             )
 
             sigma_xr = xr.DataArray(
+                # TOF_Low_model_masses_sigma
                 name=f"{variable}_model_masses_sigma",
                 data=[mass_sigmas],
                 dims=("epoch", "mass_number"),
@@ -351,6 +444,7 @@ def fit_tof_model(self, variable, peak_prominence):
             )
 
             gamma_xr = xr.DataArray(
+                # TOF_Low_model_masses_gamma
                 name=f"{variable}_model_masses_gamma",
                 data=[mass_gammas],
                 dims=("epoch", "mass_number"),
@@ -372,20 +466,39 @@ def fit_tof_model(self, variable, peak_prominence):
         return xr.concat(tof_model_parameters_list, dim="epoch")
 
     # Fit the exponentially modified Gaussian
-    def fit_expgaussian(self, x, y):
+    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.best_values
+        return result
+        # return result.best_values
 
-    def write_l2_cdf(self):
+    def write_l2_cdf(self) -> str:
         """
         Function/method description.
+
+        Returns
+        -------
+        l2_file_name : str
+            The file name of the l2 file.
         """
         # TODO: Do I need a get_global_attributes line here?
         # self.l2_data.attrs = idex_cdf_attrs.idex_l2_global_attrs
@@ -483,7 +596,8 @@ def write_l2_cdf(self):
                     "FIELDNAM": var,
                     "LABLAXIS": var,
                     "VAR_NOTES": f"The sigma of the fitted exponentially modified "
-                    f"gaussian to the first 50 peaks in {var.replace('_model_masses_sigma', '')}",
+                    f"gaussian to the first 50 peaks in "
+                    f"{var.replace('_model_masses_sigma', '')}",
                 }
                 # | idex_cdf_attrs.tof_model_dimensionless_base
                 # self.l2_attrs.get_variable_attributes("tof_model_dimensionless_base")
@@ -493,8 +607,9 @@ def write_l2_cdf(self):
                     "CATDESC": var,
                     "FIELDNAM": var,
                     "LABLAXIS": var,
-                    "VAR_NOTES": f"The gamma of the fitted exponentially modified "
-                    f"gaussian to the first 50 peaks in {var.replace('_model_masses_gamma', '')}",
+                    "VAR_NOTES": f"The gamma of the fitted exponentially modified"
+                    f" gaussian to the first 50 peaks in "
+                    f"{var.replace('_model_masses_gamma', '')}",
                 }
                 # | idex_cdf_attrs.tof_model_dimensionless_base
                 # self.l2_attrs.get_variable_attributes("tof_model_dimensionless_base")
@@ -505,35 +620,33 @@ def write_l2_cdf(self):
 
         return l2_file_name
 
-    def process_idex_l2(self, l1_file: str, data_version: str):
+    def process_idex_l2(self, l1_file: str, data_version: str) -> str:
         """
         Function/method description.
 
         Parameters
         ----------
-        l1_file: str
-            Something
+        l1_file : str
+            Something.
 
-        data_version: str
-            Something
+        data_version : str
+            Something.
 
         Returns
         -------
-        l2_cdf_file_name
+        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)
 
diff --git a/imap_processing/tests/idex/test_idex_l2.py b/imap_processing/tests/idex/test_idex_l2.py
index 4b7150e34..4717fbe76 100644
--- a/imap_processing/tests/idex/test_idex_l2.py
+++ b/imap_processing/tests/idex/test_idex_l2.py
@@ -3,9 +3,11 @@
 import pytest
 
 from imap_processing import imap_module_directory
-from imap_processing.cdf.utils import write_cdf
+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 read_and_return
+from imap_processing.idex.l2.idex_l2 import L2Processor
+
+# from imap_processing.idex.l2.idex_l2 import read_and_return
 
 
 @pytest.fixture()
@@ -28,7 +30,6 @@ def l1_cdf(decom_test_data):
     return write_cdf(decom_test_data)
 
 
-def test_read_and_return(l1_cdf):
-    dataset = read_and_return(l1_cdf)
-
-    assert dataset == decom_test_data
+def test_l1_xarray(l1_cdf):
+    l1_data = L2Processor(l1_cdf, "v001").l1_data
+    assert l1_data == load_cdf(l1_cdf)

From 9cc41af61b7d60c3e968ab4187c85a73f991485f Mon Sep 17 00:00:00 2001
From: Ana Manica <ana@manica.org>
Date: Thu, 15 Aug 2024 11:49:31 -0600
Subject: [PATCH 3/7] More .yaml cdf files

---
 .../config/imap_idex_l2_variable_attrs.yaml   |  19 ++-
 imap_processing/idex/l2/idex_l2.py            | 151 ++++--------------
 2 files changed, 50 insertions(+), 120 deletions(-)

diff --git a/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml b/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml
index 8617f78b7..369a1c995 100644
--- a/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml
+++ b/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml
@@ -85,6 +85,19 @@ 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
@@ -122,9 +135,9 @@ ion_grid_model_discharge_time:
 
 tof_low_model_masses_amplitude:
   <<: *tof_model_amplitude_base
-  CATDESC: ion_grid_model_discharge_time
-  FIELDNAM: ion_grid_model_discharge_time
-  LABLAXIS: ion_grid_model_discharge_time
+  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:
diff --git a/imap_processing/idex/l2/idex_l2.py b/imap_processing/idex/l2/idex_l2.py
index f88ffe0fd..666db9c58 100644
--- a/imap_processing/idex/l2/idex_l2.py
+++ b/imap_processing/idex/l2/idex_l2.py
@@ -73,8 +73,6 @@ def __init__(self, l1_file: str, data_version: str):
         # Now l1 data is stored in a xarray.
         self.l1_data = load_cdf(Path(l1_file))
 
-        print(self.l1_data["Target_High"])
-
         # TODO: Perhaps I want to make this just code inside the
         #  init rather than a function?
         self.l2_attrs = get_idex_attrs(data_version)
@@ -175,6 +173,8 @@ def model_fitter(
                 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
@@ -182,6 +182,9 @@ def model_fitter(
                 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(
@@ -191,6 +194,9 @@ def model_fitter(
                 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(
@@ -200,6 +206,9 @@ def model_fitter(
                 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(
@@ -209,6 +218,9 @@ def model_fitter(
                 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(
@@ -218,6 +230,9 @@ def model_fitter(
                 data=[discharge_time_fit],
                 dims=("epoch"),
                 # TODO: attrs
+                attrs=self.l2_attrs.get_variable_attributes(
+                    f"{variable_lower}_discharge_time"
+                ),
             )
 
             fit_uncertainty_xr = xr.DataArray(
@@ -227,6 +242,9 @@ def model_fitter(
                 data=[fit_uncertainty],
                 dims=("epoch"),
                 # TODO: attrs
+                attrs=self.l2_attrs.get_variable_attributes(
+                    f"{variable_lower}_model_uncertainty"
+                ),
             )
 
             model_fit_list.append(
@@ -419,12 +437,17 @@ def fit_tof_model(self, variable: str, peak_prominence: int) -> xr:
                         "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(
@@ -433,6 +456,9 @@ def fit_tof_model(self, variable: str, peak_prominence: int) -> xr:
                 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(
@@ -441,6 +467,9 @@ def fit_tof_model(self, variable: str, peak_prominence: int) -> xr:
                 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(
@@ -449,6 +478,9 @@ def fit_tof_model(self, variable: str, peak_prominence: int) -> xr:
                 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(
@@ -489,7 +521,6 @@ def fit_expgaussian(self, x: str, y: str) -> ModelResult:
         )
         result = model.fit(y, params, x=x)
         return result
-        # return result.best_values
 
     def write_l2_cdf(self) -> str:
         """
@@ -500,120 +531,6 @@ def write_l2_cdf(self) -> str:
         l2_file_name : str
             The file name of the l2 file.
         """
-        # TODO: Do I need a get_global_attributes line here?
-        # self.l2_data.attrs = idex_cdf_attrs.idex_l2_global_attrs
-
-        for var in self.l2_data:
-            if "_model_amplitude" in var:
-                self.l2_data[var].attrs = {
-                    "CATDESC": var,
-                    "FIELDNAM": var,
-                    "LABLAXIS": var,
-                    "VAR_NOTES": f"The amplitude of the response for "
-                    f"{var.replace('_model_amplitude', '')}",
-                }
-                # | idex_cdf_attrs.model_amplitude_base
-                # self.l2_attrs.get_variable_attributes("model_amplitude_base")
-
-            if "_model_uncertainty" in var:
-                self.l2_data[var].attrs = {
-                    "CATDESC": var,
-                    "FIELDNAM": var,
-                    "LABLAXIS": var,
-                    "VAR_NOTES": f"The uncertainty in the model of the response for "
-                    f"{var.replace('_model_amplitude', '')}",
-                }
-                # | idex_cdf_attrs.model_dimensionless_base
-                # self.l2_attrs.get_variable_attributes("model_dimensionless_base")
-
-            if "_model_constant_offset" in var:
-                self.l2_data[var].attrs = {
-                    "CATDESC": var,
-                    "FIELDNAM": var,
-                    "VAR_NOTES": f"The constant offset of the response for "
-                    f"{var.replace('_model_constant_offset', '')}",
-                }
-                # | idex_cdf_attrs.model_amplitude_base
-                # self.l2_attrs.get_variable_attributes("model_amplitude_base")
-
-            if "_model_time_of_impact" in var:
-                self.l2_data[var].attrs = {
-                    "CATDESC": var,
-                    "FIELDNAM": var,
-                    "LABLAXIS": var,
-                    "VAR_NOTES": f"The time of impact for "
-                    f"{var.replace('_model_time_of_impact', '')}",
-                }
-                # | idex_cdf_attrs.model_time_base
-                # self.l2_attrs.get_variable_attributes("model_time_base")
-
-            if "_model_rise_time" in var:
-                self.l2_data[var].attrs = {
-                    "CATDESC": var,
-                    "FIELDNAM": var,
-                    "LABLAXIS": var,
-                    "VAR_NOTES": f"The rise time of the response for "
-                    f"{var.replace('_model_rise_time', '')}",
-                }
-                # | idex_cdf_attrs.model_time_base
-                # self.l2_attrs.get_variable_attributes("model_time_base")
-
-            if "_model_discharge_time" in var:
-                self.l2_data[var].attrs = {
-                    "CATDESC": var,
-                    "FIELDNAM": var,
-                    "LABLAXIS": var,
-                    "VAR_NOTES": f"The discharge time of the response for "
-                    f"{var.replace('_model_discharge_time', '')}",
-                }
-                # | idex_cdf_attrs.model_time_base
-                # self.l2_attrs.get_variable_attributes("model_time_base")
-            if "_model_masses_amplitude" in var:
-                self.l2_data[var].attrs = {
-                    "CATDESC": var,
-                    "FIELDNAM": var,
-                    "LABLAXIS": var,
-                    "VAR_NOTES": f"The amplitude of the first 50 peaks in "
-                    f"{var.replace('_Model_Masses_Amplitude', '')}",
-                }
-                # | idex_cdf_attrs.tof_model_amplitude_base
-                # self.l2_attrs.get_variable_attributes("tof_model_amplitude_base")
-
-            if "_model_masses_center" in var:
-                self.l2_data[var].attrs = {
-                    "CATDESC": var,
-                    "FIELDNAM": var,
-                    "LABLAXIS": var,
-                    "VAR_NOTES": f"The center of the first 50 peaks in "
-                    f"{var.replace('_model_masses_center', '')}",
-                }
-                # | idex_cdf_attrs.tof_model_dimensionless_base
-                # self.l2_attrs.get_variable_attributes("tof_model_amplitude_base")
-
-            if "_model_masses_sigma" in var:
-                self.l2_data[var].attrs = {
-                    "CATDESC": var,
-                    "FIELDNAM": var,
-                    "LABLAXIS": var,
-                    "VAR_NOTES": f"The sigma of the fitted exponentially modified "
-                    f"gaussian to the first 50 peaks in "
-                    f"{var.replace('_model_masses_sigma', '')}",
-                }
-                # | idex_cdf_attrs.tof_model_dimensionless_base
-                # self.l2_attrs.get_variable_attributes("tof_model_dimensionless_base")
-
-            if "_model_masses_gamma" in var:
-                self.l2_data[var].attrs = {
-                    "CATDESC": var,
-                    "FIELDNAM": var,
-                    "LABLAXIS": var,
-                    "VAR_NOTES": f"The gamma of the fitted exponentially modified"
-                    f" gaussian to the first 50 peaks in "
-                    f"{var.replace('_model_masses_gamma', '')}",
-                }
-                # | idex_cdf_attrs.tof_model_dimensionless_base
-                # self.l2_attrs.get_variable_attributes("tof_model_dimensionless_base")
-
         l2_file_name = self.l1_file.replace("_l1_", "_l2_")
 
         xarray_to_cdf(self.l2_data, l2_file_name)

From 846281d629369be6a0167737fef1e0674eb76dab Mon Sep 17 00:00:00 2001
From: Ana Manica <ana@manica.org>
Date: Thu, 15 Aug 2024 13:55:53 -0600
Subject: [PATCH 4/7] More little changes

---
 .../cdf/config/imap_idex_l2_variable_attrs.yaml            | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml b/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml
index 369a1c995..2b8ac177d 100644
--- a/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml
+++ b/imap_processing/cdf/config/imap_idex_l2_variable_attrs.yaml
@@ -2,11 +2,10 @@
 model_base: &model_base
   DEPEND_0: epoch
   DISPLAY_TYPE: time_series
-  FILLVAL: np.array([-1.e+31], dtype=np.float32),
+  FILLVAL: -1.0e+31
   FORMAT: E12.2
-  # TODO: make these not...
-  VALIDMIN: np.array([-1.e+31], dtype=np.float32),
-  VALIDMAX: np.array([1.e+31], dtype=np.float32),
+  VALIDMIN: -1.0e+31
+  VALIDMAX: -1.0e+31
   VAR_TYPE: data
   SCALETYP: linear
 

From 0e65fe43b14bf513fc227ebc8dc39ddaaea305d5 Mon Sep 17 00:00:00 2001
From: Ana Manica <ana@manica.org>
Date: Thu, 15 Aug 2024 14:19:07 -0600
Subject: [PATCH 5/7] First pass at integration

---
 imap_processing/idex/l2/idex_l2.py | 52 ++++++++++++++----------------
 1 file changed, 24 insertions(+), 28 deletions(-)

diff --git a/imap_processing/idex/l2/idex_l2.py b/imap_processing/idex/l2/idex_l2.py
index 666db9c58..76f8b5460 100644
--- a/imap_processing/idex/l2/idex_l2.py
+++ b/imap_processing/idex/l2/idex_l2.py
@@ -12,14 +12,15 @@
 import lmfit
 import numpy as np
 import xarray as xr
-from cdflib.xarray import xarray_to_cdf
+
+# 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
+from imap_processing.cdf.utils import load_cdf, write_cdf
 from imap_processing.idex import constants
 
 
@@ -127,7 +128,7 @@ def model_fitter(
                 name="epoch",
                 # TODO: What should the impact time be? in RawDustEvents
                 data=[impact["epoch"].data],
-                dims=("epoch"),
+                dims="epoch",
                 # TODO: Double check that this is correct
                 attrs=self.l2_attrs.get_variable_attributes("epoch"),
             )
@@ -180,7 +181,7 @@ def model_fitter(
                 # Ion_Grid_model_time_of_impact
                 name=f"{variable}_model_time_of_impact",
                 data=[time_of_impact_fit],
-                dims=("epoch"),
+                dims="epoch",
                 # TODO: attrs
                 attrs=self.l2_attrs.get_variable_attributes(
                     f"{variable_lower}_model_time_of_impact"
@@ -192,7 +193,7 @@ def model_fitter(
                 # Ion_Grid_model_time_of_impact
                 name=f"{variable}_model_constant_offset",
                 data=[constant_offset_fit],
-                dims=("epoch"),
+                dims="epoch",
                 # TODO: attrs
                 attrs=self.l2_attrs.get_variable_attributes(
                     f"{variable_lower}__model_constant_offset"
@@ -204,7 +205,7 @@ def model_fitter(
                 # Ion_Grid_model_amplitude
                 name=f"{variable}_model_amplitude",
                 data=[amplitude_fit],
-                dims=("epoch"),
+                dims="epoch",
                 # TODO: attrs
                 attrs=self.l2_attrs.get_variable_attributes(
                     f"{variable_lower}_model_amplitude"
@@ -216,7 +217,7 @@ def model_fitter(
                 # Ion_Grid_model_rise_time
                 name=f"{variable}_model_rise_time",
                 data=[rise_time_fit],
-                dims=("epoch"),
+                dims="epoch",
                 # TODO: attrs
                 attrs=self.l2_attrs.get_variable_attributes(
                     f"{variable_lower}_model_rise_time"
@@ -228,7 +229,7 @@ def model_fitter(
                 # Ion_Grid_model_discharge_time
                 name=f"{variable}_model_discharge_time",
                 data=[discharge_time_fit],
-                dims=("epoch"),
+                dims="epoch",
                 # TODO: attrs
                 attrs=self.l2_attrs.get_variable_attributes(
                     f"{variable_lower}_discharge_time"
@@ -240,7 +241,7 @@ def model_fitter(
                 # Ion_Grid_model_uncertainty
                 name=f"{variable}_model_uncertainty",
                 data=[fit_uncertainty],
-                dims=("epoch"),
+                dims="epoch",
                 # TODO: attrs
                 attrs=self.l2_attrs.get_variable_attributes(
                     f"{variable_lower}_model_uncertainty"
@@ -395,7 +396,7 @@ def fit_tof_model(self, variable: str, peak_prominence: int) -> xr:
         mass_number_xr = xr.DataArray(
             name="mass_number",
             data=np.linspace(1, 50, 50),
-            dims=("mass_number"),
+            dims="mass_number",
             # TODO: check this is correct
             attrs=self.l2_attrs.get_variable_attributes("mass_number_attrs"),
         )
@@ -406,7 +407,7 @@ def fit_tof_model(self, variable: str, peak_prominence: int) -> xr:
                 name="epoch",
                 # TODO: What should the impact time be? in RawDustEvents
                 data=[impact["epoch"].data],
-                dims=("epoch"),
+                dims="epoch",
                 attrs=self.l2_attrs.get_variable_attributes("epoch"),
             )
 
@@ -522,20 +523,17 @@ def fit_expgaussian(self, x: str, y: str) -> ModelResult:
         result = model.fit(y, params, x=x)
         return result
 
-    def write_l2_cdf(self) -> str:
-        """
-        Function/method description.
-
-        Returns
-        -------
-        l2_file_name : str
-            The file name of the l2 file.
-        """
-        l2_file_name = self.l1_file.replace("_l1_", "_l2_")
-
-        xarray_to_cdf(self.l2_data, l2_file_name)
-
-        return l2_file_name
+    # 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:
         """
@@ -567,6 +565,4 @@ def process_idex_l2(self, l1_file: str, data_version: str) -> str:
         """
         l2_data = L2Processor(l1_file, data_version)
 
-        l2_cdf_file_name = l2_data.write_l2_cdf()
-
-        return l2_cdf_file_name
+        return str(write_cdf(l2_data.l2_data))

From 6c88e7692e131ec8edf17f8a86b4a57a122e48bc Mon Sep 17 00:00:00 2001
From: Ana Manica <ana@manica.org>
Date: Thu, 15 Aug 2024 14:43:34 -0600
Subject: [PATCH 6/7] Adding lmfit

---
 poetry.lock    | 117 ++++++++++++++++++++++++++++++++++++++++++++++++-
 pyproject.toml |   1 +
 2 files changed, 117 insertions(+), 1 deletion(-)

diff --git a/poetry.lock b/poetry.lock
index 4f8f4bb36..8ca6020a6 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -29,6 +29,23 @@ 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", "twine"]
+doc = ["Sphinx"]
+test = ["coverage", "pytest", "pytest-cov"]
+
 [[package]]
 name = "attrs"
 version = "23.2.0"
@@ -456,6 +473,21 @@ files = [
     {file = "deepmerge-1.1.1.tar.gz", hash = "sha256:53a489dc9449636e480a784359ae2aab3191748c920649551c8e378622f0eca4"},
 ]
 
+[[package]]
+name = "dill"
+version = "0.3.8"
+description = "serialize all of Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
+    {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
+]
+
+[package.extras]
+graph = ["objgraph (>=1.7.2)"]
+profile = ["gprof2dot (>=2022.7.29)"]
+
 [[package]]
 name = "distlib"
 version = "0.3.8"
@@ -651,6 +683,30 @@ files = [
 [package.dependencies]
 referencing = ">=0.31.0"
 
+[[package]]
+name = "lmfit"
+version = "1.3.2"
+description = "Least-Squares Minimization with Bounds and Constraints"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "lmfit-1.3.2-py3-none-any.whl", hash = "sha256:2b834f054cd7a5172f3b431345b292e5d95ea387d6f96d60ad35a11b8efee6ac"},
+    {file = "lmfit-1.3.2.tar.gz", hash = "sha256:31beeae1f027c1b8c14dcd7f2e8488a80b75fb389e77fca677549bdc2fe597bb"},
+]
+
+[package.dependencies]
+asteval = ">=1.0"
+dill = ">=0.3.4"
+numpy = ">=1.19"
+scipy = ">=1.6"
+uncertainties = ">=3.2.2"
+
+[package.extras]
+all = ["lmfit[dev,doc,test]"]
+dev = ["build", "check-wheel-contents", "flake8-pyproject", "pre-commit", "twine"]
+doc = ["Pillow", "Sphinx", "cairosvg", "corner", "emcee (>=3.0.0)", "ipykernel", "jupyter-sphinx (>=0.2.4)", "matplotlib", "numdifftools", "numexpr", "pandas", "pycairo", "sphinx-gallery (>=0.10)", "sphinxcontrib-svg2pdfconverter", "sympy"]
+test = ["coverage", "flaky", "pytest", "pytest-cov"]
+
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
@@ -1361,6 +1417,48 @@ files = [
     {file = "ruff-0.2.1.tar.gz", hash = "sha256:3b42b5d8677cd0c72b99fcaf068ffc62abb5a19e71b4a3b9cfa50658a0af02f1"},
 ]
 
+[[package]]
+name = "scipy"
+version = "1.13.1"
+description = "Fundamental algorithms for scientific computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "scipy-1.13.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:20335853b85e9a49ff7572ab453794298bcf0354d8068c5f6775a0eabf350aca"},
+    {file = "scipy-1.13.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:d605e9c23906d1994f55ace80e0125c587f96c020037ea6aa98d01b4bd2e222f"},
+    {file = "scipy-1.13.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cfa31f1def5c819b19ecc3a8b52d28ffdcc7ed52bb20c9a7589669dd3c250989"},
+    {file = "scipy-1.13.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f26264b282b9da0952a024ae34710c2aff7d27480ee91a2e82b7b7073c24722f"},
+    {file = "scipy-1.13.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:eccfa1906eacc02de42d70ef4aecea45415f5be17e72b61bafcfd329bdc52e94"},
+    {file = "scipy-1.13.1-cp310-cp310-win_amd64.whl", hash = "sha256:2831f0dc9c5ea9edd6e51e6e769b655f08ec6db6e2e10f86ef39bd32eb11da54"},
+    {file = "scipy-1.13.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:27e52b09c0d3a1d5b63e1105f24177e544a222b43611aaf5bc44d4a0979e32f9"},
+    {file = "scipy-1.13.1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:54f430b00f0133e2224c3ba42b805bfd0086fe488835effa33fa291561932326"},
+    {file = "scipy-1.13.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e89369d27f9e7b0884ae559a3a956e77c02114cc60a6058b4e5011572eea9299"},
+    {file = "scipy-1.13.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a78b4b3345f1b6f68a763c6e25c0c9a23a9fd0f39f5f3d200efe8feda560a5fa"},
+    {file = "scipy-1.13.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:45484bee6d65633752c490404513b9ef02475b4284c4cfab0ef946def50b3f59"},
+    {file = "scipy-1.13.1-cp311-cp311-win_amd64.whl", hash = "sha256:5713f62f781eebd8d597eb3f88b8bf9274e79eeabf63afb4a737abc6c84ad37b"},
+    {file = "scipy-1.13.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:5d72782f39716b2b3509cd7c33cdc08c96f2f4d2b06d51e52fb45a19ca0c86a1"},
+    {file = "scipy-1.13.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:017367484ce5498445aade74b1d5ab377acdc65e27095155e448c88497755a5d"},
+    {file = "scipy-1.13.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:949ae67db5fa78a86e8fa644b9a6b07252f449dcf74247108c50e1d20d2b4627"},
+    {file = "scipy-1.13.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:de3ade0e53bc1f21358aa74ff4830235d716211d7d077e340c7349bc3542e884"},
+    {file = "scipy-1.13.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:2ac65fb503dad64218c228e2dc2d0a0193f7904747db43014645ae139c8fad16"},
+    {file = "scipy-1.13.1-cp312-cp312-win_amd64.whl", hash = "sha256:cdd7dacfb95fea358916410ec61bbc20440f7860333aee6d882bb8046264e949"},
+    {file = "scipy-1.13.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:436bbb42a94a8aeef855d755ce5a465479c721e9d684de76bf61a62e7c2b81d5"},
+    {file = "scipy-1.13.1-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:8335549ebbca860c52bf3d02f80784e91a004b71b059e3eea9678ba994796a24"},
+    {file = "scipy-1.13.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d533654b7d221a6a97304ab63c41c96473ff04459e404b83275b60aa8f4b7004"},
+    {file = "scipy-1.13.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:637e98dcf185ba7f8e663e122ebf908c4702420477ae52a04f9908707456ba4d"},
+    {file = "scipy-1.13.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:a014c2b3697bde71724244f63de2476925596c24285c7a637364761f8710891c"},
+    {file = "scipy-1.13.1-cp39-cp39-win_amd64.whl", hash = "sha256:392e4ec766654852c25ebad4f64e4e584cf19820b980bc04960bca0b0cd6eaa2"},
+    {file = "scipy-1.13.1.tar.gz", hash = "sha256:095a87a0312b08dfd6a6155cbbd310a8c51800fc931b8c0b84003014b874ed3c"},
+]
+
+[package.dependencies]
+numpy = ">=1.22.4,<2.3"
+
+[package.extras]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
+test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -1662,6 +1760,23 @@ files = [
     {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
+[[package]]
+name = "uncertainties"
+version = "3.2.2"
+description = "calculations with values with uncertainties, error propagation"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "uncertainties-3.2.2-py3-none-any.whl", hash = "sha256:fd8543355952f4052786ed4150acaf12e23117bd0f5bd03ea07de466bce646e7"},
+    {file = "uncertainties-3.2.2.tar.gz", hash = "sha256:e62c86fdc64429828229de6ab4e11466f114907e6bd343c077858994cc12e00b"},
+]
+
+[package.extras]
+all = ["uncertainties[arrays,doc,test]"]
+arrays = ["numpy"]
+doc = ["python-docs-theme", "sphinx", "sphinx-copybutton"]
+test = ["pytest", "pytest-cov"]
+
 [[package]]
 name = "urllib3"
 version = "2.2.2"
@@ -1747,4 +1862,4 @@ tools = ["openpyxl", "pandas"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4"
-content-hash = "28cbb877e15c362c80541a8cebd35f12575cd86802c902505b4db1f65640d37e"
+content-hash = "619d7437d572c3858690ecd66eca55fec9cbd5d2ea3de813d45b9d59618de958"
diff --git a/pyproject.toml b/pyproject.toml
index df0ef2a95..ac5ae31a9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,6 +39,7 @@ spiceypy = ">=6.0.0"
 xarray = '>=2023.0.0'
 pyyaml = "^6.0.1"
 numpy = "^1.26.4"
+lmfit = '>=1.3.2'
 
 # Optional dependencies
 numpydoc = {version="^1.5.0", optional=true}

From fcc9c3398183bee91bfd9f29f710ebbf5bc8c575 Mon Sep 17 00:00:00 2001
From: Ana Manica <ana@manica.org>
Date: Thu, 15 Aug 2024 14:52:57 -0600
Subject: [PATCH 7/7] Adding lmfit

---
 imap_processing/idex/l2/idex_l2.py |   4 +-
 poetry.lock                        | 678 +++++++++++++++--------------
 2 files changed, 354 insertions(+), 328 deletions(-)

diff --git a/imap_processing/idex/l2/idex_l2.py b/imap_processing/idex/l2/idex_l2.py
index 76f8b5460..443bbc072 100644
--- a/imap_processing/idex/l2/idex_l2.py
+++ b/imap_processing/idex/l2/idex_l2.py
@@ -196,7 +196,7 @@ def model_fitter(
                 dims="epoch",
                 # TODO: attrs
                 attrs=self.l2_attrs.get_variable_attributes(
-                    f"{variable_lower}__model_constant_offset"
+                    f"{variable_lower}_model_constant_offset"
                 ),
             )
 
@@ -232,7 +232,7 @@ def model_fitter(
                 dims="epoch",
                 # TODO: attrs
                 attrs=self.l2_attrs.get_variable_attributes(
-                    f"{variable_lower}_discharge_time"
+                    f"{variable_lower}_model_discharge_time"
                 ),
             )
 
diff --git a/poetry.lock b/poetry.lock
index 8ca6020a6..7e1f4379d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "accessible-pygments"
@@ -48,32 +48,32 @@ test = ["coverage", "pytest", "pytest-cov"]
 
 [[package]]
 name = "attrs"
-version = "23.2.0"
+version = "24.2.0"
 description = "Classes Without Boilerplate"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
-    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
+    {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
 ]
 
 [package.extras]
-cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[tests]", "pre-commit"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
-tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
-tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "babel"
-version = "2.15.0"
+version = "2.16.0"
 description = "Internationalization utilities"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "Babel-2.15.0-py3-none-any.whl", hash = "sha256:08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb"},
-    {file = "babel-2.15.0.tar.gz", hash = "sha256:8daf0e265d05768bc6c7a314cf1321e9a123afc328cc635c18622a2f30a04413"},
+    {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"},
+    {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"},
 ]
 
 [package.extras]
@@ -265,13 +265,13 @@ tests = ["astropy", "hypothesis", "pytest (>=3.9)", "pytest-cov", "pytest-remote
 
 [[package]]
 name = "certifi"
-version = "2024.6.2"
+version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = true
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
-    {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
+    {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
+    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
 ]
 
 [[package]]
@@ -397,63 +397,83 @@ files = [
 
 [[package]]
 name = "coverage"
-version = "7.5.4"
+version = "7.6.1"
 description = "Code coverage measurement for Python"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "coverage-7.5.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6cfb5a4f556bb51aba274588200a46e4dd6b505fb1a5f8c5ae408222eb416f99"},
-    {file = "coverage-7.5.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2174e7c23e0a454ffe12267a10732c273243b4f2d50d07544a91198f05c48f47"},
-    {file = "coverage-7.5.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2214ee920787d85db1b6a0bd9da5f8503ccc8fcd5814d90796c2f2493a2f4d2e"},
-    {file = "coverage-7.5.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1137f46adb28e3813dec8c01fefadcb8c614f33576f672962e323b5128d9a68d"},
-    {file = "coverage-7.5.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b385d49609f8e9efc885790a5a0e89f2e3ae042cdf12958b6034cc442de428d3"},
-    {file = "coverage-7.5.4-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b4a474f799456e0eb46d78ab07303286a84a3140e9700b9e154cfebc8f527016"},
-    {file = "coverage-7.5.4-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:5cd64adedf3be66f8ccee418473c2916492d53cbafbfcff851cbec5a8454b136"},
-    {file = "coverage-7.5.4-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:e564c2cf45d2f44a9da56f4e3a26b2236504a496eb4cb0ca7221cd4cc7a9aca9"},
-    {file = "coverage-7.5.4-cp310-cp310-win32.whl", hash = "sha256:7076b4b3a5f6d2b5d7f1185fde25b1e54eb66e647a1dfef0e2c2bfaf9b4c88c8"},
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 ]
 
 [package.dependencies]
@@ -523,13 +543,13 @@ files = [
 
 [[package]]
 name = "exceptiongroup"
-version = "1.2.1"
+version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = true
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -553,13 +573,13 @@ typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "identify"
-version = "2.5.36"
+version = "2.6.0"
 description = "File identification library for Python"
 optional = true
 python-versions = ">=3.8"
 files = [
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 [package.extras]
@@ -589,12 +609,12 @@ files = [
 
 [[package]]
 name = "imap-data-access"
-version = "0.7.0"
+version = "0.8.0"
 description = "IMAP SDC Data Access"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -603,13 +623,13 @@ test = ["pytest", "pytest-cov"]
 
 [[package]]
 name = "importlib-metadata"
-version = "8.0.0"
+version = "8.2.0"
 description = "Read metadata from Python packages"
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -650,13 +670,13 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "jsonschema"
-version = "4.22.0"
+version = "4.23.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = true
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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 [[package]]
 name = "jsonschema-specifications"
@@ -789,44 +809,44 @@ files = [
 
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 python-versions = ">=3.8"
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 [package.dependencies]
 mypy-extensions = ">=1.0.0"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-typing-extensions = ">=4.1.0"
+typing-extensions = ">=4.6.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
@@ -903,13 +923,13 @@ files = [
 
 [[package]]
 name = "numpydoc"
-version = "1.7.0"
+version = "1.8.0"
 description = "Sphinx extension to support docstrings in Numpy format"
 optional = true
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 test = ["matplotlib", "pytest", "pytest-cov"]
 
 [[package]]
@@ -1064,13 +1084,13 @@ testing = ["pytest", "pytest-benchmark"]
 
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 [[package]]
@@ -1285,110 +1307,114 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
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 [[package]]
@@ -1483,13 +1509,13 @@ files = [
 
 [[package]]
 name = "soupsieve"
-version = "2.5"
+version = "2.6"
 description = "A modern CSS selector implementation for Beautiful Soup."
 optional = true
 python-versions = ">=3.8"
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 [[package]]
@@ -1508,27 +1534,27 @@ bitstring = ">=4.0.1"
 
 [[package]]
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-Pygments = ">=2.14"
-requests = ">=2.25.0"
-snowballstemmer = ">=2.0"
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 sphinxcontrib-applehelp = "*"
 sphinxcontrib-devhelp = "*"
 sphinxcontrib-htmlhelp = ">=2.0.0"
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 mistune = ">=3.0,<4.0"
 pygments = ">=2.8"
 sphinx = ">=6"
@@ -1564,49 +1590,49 @@ dev = ["attribution (==1.7.1)", "black (==24.4.2)", "coverage (==7.5.1)", "docut
 
 [[package]]
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 standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
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 python-versions = ">=3.9"
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 [package.extras]
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 standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
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 description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
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 [package.extras]
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 standalone = ["Sphinx (>=5)"]
 test = ["html5lib", "pytest"]
 
@@ -1661,33 +1687,33 @@ sphinxcontrib-httpdomain = ">=1.5.0"
 
 [[package]]
 name = "sphinxcontrib-qthelp"
-version = "1.0.7"
+version = "2.0.0"
 description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp documents"
 optional = true
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_qthelp-1.0.7-py3-none-any.whl", hash = "sha256:e2ae3b5c492d58fcbd73281fbd27e34b8393ec34a073c792642cd8e529288182"},
-    {file = "sphinxcontrib_qthelp-1.0.7.tar.gz", hash = "sha256:053dedc38823a80a7209a80860b16b722e9e0209e32fea98c90e4e6624588ed6"},
+    {file = "sphinxcontrib_qthelp-2.0.0-py3-none-any.whl", hash = "sha256:b18a828cdba941ccd6ee8445dbe72ffa3ef8cbe7505d8cd1fa0d42d3f2d5f3eb"},
+    {file = "sphinxcontrib_qthelp-2.0.0.tar.gz", hash = "sha256:4fe7d0ac8fc171045be623aba3e2a8f613f8682731f9153bb2e40ece16b9bbab"},
 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
-test = ["pytest"]
+test = ["defusedxml (>=0.7.1)", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-serializinghtml"
-version = "1.1.10"
+version = "2.0.0"
 description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)"
 optional = true
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_serializinghtml-1.1.10-py3-none-any.whl", hash = "sha256:326369b8df80a7d2d8d7f99aa5ac577f51ea51556ed974e7716cfd4fca3f6cb7"},
-    {file = "sphinxcontrib_serializinghtml-1.1.10.tar.gz", hash = "sha256:93f3f5dc458b91b192fe10c397e324f262cf163d79f3282c158e8436a2c4511f"},
+    {file = "sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl", hash = "sha256:6e2cb0eef194e10c27ec0023bfeb25badbbb5868244cf5bc5bdc04e4464bf331"},
+    {file = "sphinxcontrib_serializinghtml-2.0.0.tar.gz", hash = "sha256:e9d912827f872c029017a53f0ef2180b327c3f7fd23c87229f7a8e8b70031d4d"},
 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
@@ -1816,13 +1842,13 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [[package]]
 name = "xarray"
-version = "2024.6.0"
+version = "2024.7.0"
 description = "N-D labeled arrays and datasets in Python"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "xarray-2024.6.0-py3-none-any.whl", hash = "sha256:721a7394e8ec3d592b2d8ebe21eed074ac077dc1bb1bd777ce00e41700b4866c"},
-    {file = "xarray-2024.6.0.tar.gz", hash = "sha256:0b91e0bc4dc0296947947640fe31ec6e867ce258d2f7cbc10bedf4a6d68340c7"},
+    {file = "xarray-2024.7.0-py3-none-any.whl", hash = "sha256:1b0fd51ec408474aa1f4a355d75c00cc1c02bd425d97b2c2e551fd21810e7f64"},
+    {file = "xarray-2024.7.0.tar.gz", hash = "sha256:4cae512d121a8522d41e66d942fb06c526bc1fd32c2c181d5fe62fe65b671638"},
 ]
 
 [package.dependencies]
@@ -1840,13 +1866,13 @@ viz = ["matplotlib", "nc-time-axis", "seaborn"]
 
 [[package]]
 name = "zipp"
-version = "3.19.2"
+version = "3.20.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
-    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
+    {file = "zipp-3.20.0-py3-none-any.whl", hash = "sha256:58da6168be89f0be59beb194da1250516fdaa062ccebd30127ac65d30045e10d"},
+    {file = "zipp-3.20.0.tar.gz", hash = "sha256:0145e43d89664cfe1a2e533adc75adafed82fe2da404b4bbb6b026c0157bdb31"},
 ]
 
 [package.extras]