diff --git a/imap_processing/ialirt/l0/process_swapi.py b/imap_processing/ialirt/l0/process_swapi.py
index e987f80ac..f64bbe14d 100644
--- a/imap_processing/ialirt/l0/process_swapi.py
+++ b/imap_processing/ialirt/l0/process_swapi.py
@@ -14,7 +14,7 @@
 from imap_processing.ialirt.utils.time import calculate_time
 from imap_processing.spice.time import met_to_ttj2000ns, met_to_utc
 from imap_processing.swapi.l1.swapi_l1 import process_sweep_data
-from imap_processing.swapi.l2.swapi_l2 import SWAPI_LIVETIME
+from imap_processing.swapi.l2.swapi_l2 import SWAPI_LIVETIME, select_first_63_passband_energies
 
 logger = logging.getLogger(__name__)
 
@@ -76,6 +76,7 @@ def optimize_pseudo_parameters(
     Find the pseudo speed (u), density (n) and temperature (T) of solar wind particles.
 
     Fit a curve to calculated count rate values as a function of energy passband.
+    Takes count rates (with errors) and energy passbands for one single sweep as input.
 
     Parameters
     ----------
@@ -90,45 +91,50 @@ def optimize_pseudo_parameters(
     -------
     solution_dict : dict
         Dictionary containing the optimized speed, density, and temperature values for
-        each sweep included in the input count_rates array.
+        the sweep included in the input count_rates array.
     """
-    solution_dict = {  # type: ignore
-        "pseudo_speed": [],
-        "pseudo_density": [],
-        "pseudo_temperature": [],
-    }
-
-    for sweep in np.arange(count_rates.shape[0]):
-        current_sweep_count_rates = count_rates[sweep, :]
-        current_sweep_count_rate_errors = count_rate_error[sweep, :]
-        # Find the max count rate, and use the 5 points surrounding it
-        max_index = np.argmax(current_sweep_count_rates)
-        initial_speed_guess = np.sqrt(energy_passbands[max_index]) * Consts.speed_coeff
-        initial_param_guess = np.array(
-            [
-                initial_speed_guess,
-                5 * (400 / initial_speed_guess) ** 2,
-                60000 * (initial_speed_guess / 400) ** 2,
-            ]
-        )
-        sol = curve_fit(
+    assert len(count_rates.shape) == 1
+    assert count_rates.shape == count_rate_error.shape == energy_passbands.shape, f'{count_rates.shape} {count_rate_error.shape} {energy_passbands.shape}'
+
+    current_sweep_count_rates = count_rates
+    current_sweep_count_rate_errors = count_rate_error
+    # Find the max count rate, and use the 5 points surrounding it
+    max_index = np.argmax(current_sweep_count_rates)
+    initial_speed_guess = np.sqrt(energy_passbands[max_index]) * Consts.speed_coeff
+    initial_param_guess = np.array(
+        [
+            initial_speed_guess,
+            5 * (400 / initial_speed_guess) ** 2,
+            60000 * (initial_speed_guess / 400) ** 2,
+        ]
+    )
+    
+    fitting_point_range = range(max_index - 3, max_index + 3)
+    xdata = energy_passbands.take(fitting_point_range, mode="clip")
+    ydata = current_sweep_count_rates.take(fitting_point_range, mode="clip")
+    sigma = current_sweep_count_rate_errors.take(fitting_point_range, mode="clip")
+    
+    # exclude points where the count is zero (or rounded to zero)
+    # because the fitting algorithm cannot handle them
+    # (since zero count implies sigma=0)
+    is_valid_data = ydata > 0
+
+    if np.count_nonzero(is_valid_data) >= 3:
+        solution = curve_fit(
             f=count_rate,
-            xdata=energy_passbands.take(
-                range(max_index - 3, max_index + 3), mode="clip"
-            ),
-            ydata=current_sweep_count_rates.take(
-                range(max_index - 3, max_index + 3), mode="clip"
-            ),
-            sigma=current_sweep_count_rate_errors.take(
-                range(max_index - 3, max_index + 3), mode="clip"
-            ),
+            xdata=xdata[is_valid_data],
+            ydata=ydata[is_valid_data],
+            sigma=sigma[is_valid_data],
             p0=initial_param_guess,
-        )
-        solution_dict["pseudo_speed"].append(sol[0][0])
-        solution_dict["pseudo_density"].append(sol[0][1])
-        solution_dict["pseudo_temperature"].append(sol[0][2])
+        )[0]
+    else:
+        solution = [np.nan] * 3
 
-    return solution_dict
+    return {
+        "pseudo_speed": solution[0],
+        "pseudo_density": solution[1],
+        "pseudo_temperature": solution[2]
+    }
 
 
 def process_swapi_ialirt(
@@ -176,6 +182,7 @@ def process_swapi_ialirt(
             (grouped_dataset["group"] == group)
         ]
 
+        # TODO change this to the center of the interval
         met_values.append(
             int(grouped_dataset["met"][(grouped_dataset["group"] == group).values][0])
         )
@@ -208,41 +215,40 @@ def process_swapi_ialirt(
         dtype="datetime64[ns]"
     )
 
-    # Find the sweep's energy data for the latest time, where sweep_id == 2
-    subset = calibration_lut_table[
-        (calibration_lut_table["timestamp"] == calibration_lut_table["timestamp"].max())
-        & (calibration_lut_table["Sweep #"] == 2)
-    ]
-    if subset.empty:
-        energy_passbands = np.full(NUM_IALIRT_ENERGY_STEPS, np.nan, dtype=np.float64)
-    else:
-        subset = subset.sort_values(["timestamp", "ESA Step #"])
-        energy_passbands = (
-            subset["Energy"][:NUM_IALIRT_ENERGY_STEPS].to_numpy().astype(float)
-        )
-
-    solution = optimize_pseudo_parameters(
-        raw_coin_rate, count_rate_error, energy_passbands
-    )
-
     swapi_data = []
 
-    for entry in np.arange(0, len(solution["pseudo_speed"])):
+    if len(met_values) != len(raw_coin_rate):
+        raise RuntimeError('Inconsistent number of MET times and count sweep bins')
+
+    for entry in np.arange(0, len(raw_coin_rate)):
+        entry_met = int(met_values[entry])
+        entry_met_in_utc = met_to_utc(entry_met)
+        sweep_table_version = unpacked_data["swapi_version"].sel(epoch=entry_met, method="nearest").item()
+        energy_passbands = select_first_63_passband_energies(
+            calibration_table_df=calibration_lut_table,
+            time=entry_met_in_utc,
+            sweep_table_version=sweep_table_version
+        )
+        solution = optimize_pseudo_parameters(
+            raw_coin_rate[entry],
+            count_rate_error[entry],
+            energy_passbands
+        )
         swapi_data.append(
             {
                 "apid": 478,
-                "met": int(met_values[entry]),
-                "met_in_utc": met_to_utc(met_values[entry]).split(".")[0],
+                "met": entry_met,
+                "met_in_utc": entry_met_in_utc.split(".")[0],
                 "ttj2000ns": int(met_to_ttj2000ns(met_values[entry])),
                 "instrument": "swapi",
                 "swapi_pseudo_proton_speed": Decimal(
-                    f"{solution['pseudo_speed'][entry]:.3f}"
+                    f"{solution['pseudo_speed']:.3f}"
                 ),
                 "swapi_pseudo_proton_density": Decimal(
-                    f"{solution['pseudo_density'][entry]:.3f}"
+                    f"{solution['pseudo_density']:.3f}"
                 ),
                 "swapi_pseudo_proton_temperature": Decimal(
-                    f"{solution['pseudo_temperature'][entry]:.3f}"
+                    f"{solution['pseudo_temperature']:.3f}"
                 ),
             }
         )
diff --git a/imap_processing/swapi/l2/swapi_l2.py b/imap_processing/swapi/l2/swapi_l2.py
index a81f89d79..4c02c21ea 100644
--- a/imap_processing/swapi/l2/swapi_l2.py
+++ b/imap_processing/swapi/l2/swapi_l2.py
@@ -1,6 +1,7 @@
 """SWAPI L2 processing module."""
 
 import logging
+import select
 
 import numpy as np
 import numpy.typing as npt
@@ -68,34 +69,7 @@ def solve_full_sweep_energy(
     first_63_energies = []
 
     for time, sweep_id in zip(data_time, sweep_table, strict=False):
-        # Find the sweep's ESA data for the given time and sweep_id
-        subset = esa_table_df[
-            (esa_table_df["timestamp"] <= time) & (esa_table_df["Sweep #"] == sweep_id)
-        ]
-        if subset.empty:
-            # Get the earliest timestamp available
-            earliest_time = esa_table_df["timestamp"].min()
-
-            # Find the sweep's ESA data for the earliest time and sweep_id
-            earliest_subset = esa_table_df[
-                (esa_table_df["timestamp"] == earliest_time)
-                & (esa_table_df["Sweep #"] == sweep_id)
-            ]
-            if earliest_subset.empty:
-                raise ValueError(
-                    f"No matching ESA table entry found for sweep ID {sweep_id} "
-                    f"at time {time}, and no entries found for earliest time "
-                    f"{earliest_time}."
-                )
-            subset = earliest_subset
-
-        # Subset data can contain multiple 72 energy values with last 9 fine energies
-        # with 'Solve' value. We need to sort by time and ESA step to maintain correct
-        # order. Then take the last group of 72 steps values and select first 63
-        # values only.
-        subset = subset.sort_values(["timestamp", "ESA Step #"])
-        grouped = subset["Energy"].values.reshape(-1, NUM_ENERGY_STEPS)
-        first_63 = grouped[-1, :63]
+        first_63 = select_first_63_passband_energies(esa_table_df, time, sweep_id)
         first_63_energies.append(first_63)
 
     # Find last 9 fine energy values of all sweeps data
@@ -163,6 +137,33 @@ def solve_full_sweep_energy(
     return sweeps_energy_value
 
 
+def select_first_63_passband_energies(calibration_table_df, time, sweep_table_version):
+    """
+    Select passband energies from the calibration table based on the time and table version.
+        
+    :param calibration_table_df:
+        DataFrame containing entries for `timestamp`, `Sweep #`, `ESA Step #`, and `Energy`.
+        Each timestamp has 72 energy steps.
+    :param time: Time of measurement (calibration from after this time is excluded if earlier is available, otherwise earliest time is used)
+    :param sweep_table_version: Sweep table version (to select from the `Sweep #` column)
+    """
+    
+    subset = calibration_table_df[calibration_table_df["Sweep #"] == sweep_table_version]
+    
+    table_timestamps = pd.to_datetime(subset["timestamp"], utc=True)
+    table_timestamps_before = table_timestamps[table_timestamps <= pd.to_datetime(time, utc=True)]
+    selected_timestamp = (table_timestamps_before.max()  # latest one that is not after the input time
+                          if not table_timestamps_before.empty
+                          else table_timestamps.max())
+    
+    subset = subset[table_timestamps == selected_timestamp]
+    
+    assert len(subset) == 72, f'{len(subset)} entries in calibration table for time {time}, sweep # {sweep_table_version}; 72 are required'
+
+    subset = subset.sort_values(["timestamp", "ESA Step #"])
+    return subset["Energy"][:63].to_numpy().astype(float)
+
+
 def swapi_l2(
     l1_dataset: xr.Dataset,
     esa_table_df: pd.DataFrame,
diff --git a/imap_processing/tests/ialirt/data/l0/imap_swapi_esa-unit-conversion_20250626_v001.csv b/imap_processing/tests/ialirt/data/l0/imap_swapi_esa-unit-conversion_20250626_v001.csv
new file mode 100644
index 000000000..ff96dc762
--- /dev/null
+++ b/imap_processing/tests/ialirt/data/l0/imap_swapi_esa-unit-conversion_20250626_v001.csv
@@ -0,0 +1,289 @@
+timestamp,ESA Step #,K factor,Voltage,Energy,Sweep #,ESA Index Number,LUT version number
+2/11/2025 0:00,0,1.88,619,"1,163",0,544,4
+2/11/2025 0:00,1,1.88,619,"1,163",0,544,4
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diff --git a/imap_processing/tests/ialirt/data/l0/imap_swapi_esa-unit-conversion_20251201_v001.csv b/imap_processing/tests/ialirt/data/l0/imap_swapi_esa-unit-conversion_20251201_v001.csv
new file mode 100644
index 000000000..24da17574
--- /dev/null
+++ b/imap_processing/tests/ialirt/data/l0/imap_swapi_esa-unit-conversion_20251201_v001.csv
@@ -0,0 +1,361 @@
+timestamp,ESA Step #,K factor,Voltage,Energy,Sweep #,ESA Index Number,LUT version number
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+12/1/2025 20:20,65,1.93,0,0,3,1023,8
+12/1/2025 20:20,66,1.93,Solve,Solve,3,-40,8
+12/1/2025 20:20,67,1.93,Solve,Solve,3,-24,8
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+12/1/2025 20:20,69,1.93,Solve,Solve,3,8,8
+12/1/2025 20:20,70,1.93,Solve,Solve,3,24,8
+12/1/2025 20:20,71,1.93,Solve,Solve,3,40,8
diff --git a/imap_processing/tests/ialirt/data/l0/iois_1_packets_2025_322_12_00_56 b/imap_processing/tests/ialirt/data/l0/iois_1_packets_2025_322_12_00_56
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diff --git a/imap_processing/tests/ialirt/data/l0/iois_1_packets_2025_343_00_00_17 b/imap_processing/tests/ialirt/data/l0/iois_1_packets_2025_343_00_00_17
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index 000000000..74296cb98
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diff --git a/imap_processing/tests/ialirt/unit/conftest.py b/imap_processing/tests/ialirt/unit/conftest.py
index 8f75a1fb7..844402750 100644
--- a/imap_processing/tests/ialirt/unit/conftest.py
+++ b/imap_processing/tests/ialirt/unit/conftest.py
@@ -19,6 +19,42 @@ def sc_packet_path():
     return packet_path, xtce_ialirt_path
 
 
+@pytest.fixture
+def swapi_presweep_sc_packet_path():
+    """Returns the spacecraft packet directory."""
+    packet_path = (
+        imap_module_directory
+        / "tests"
+        / "ialirt"
+        / "data"
+        / "l0"
+        / "iois_1_packets_2025_322_12_00_56"
+    )
+    xtce_ialirt_path = (
+        imap_module_directory / "ialirt" / "packet_definitions" / "ialirt.xml"
+    )
+
+    return packet_path, xtce_ialirt_path
+
+
+@pytest.fixture
+def swapi_postsweep_sc_packet_path():
+    """Returns the spacecraft packet directory."""
+    packet_path = (
+        imap_module_directory
+        / "tests"
+        / "ialirt"
+        / "data"
+        / "l0"
+        / "iois_1_packets_2025_343_00_00_17"
+    )
+    xtce_ialirt_path = (
+        imap_module_directory / "ialirt" / "packet_definitions" / "ialirt.xml"
+    )
+
+    return packet_path, xtce_ialirt_path
+
+
 @pytest.fixture
 def ialirt_mag_test_l1d_data():
     """Returns the MAG I-ALiRT calibration dataset."""
diff --git a/imap_processing/tests/ialirt/unit/test_process_swapi.py b/imap_processing/tests/ialirt/unit/test_process_swapi.py
index db1e5778a..a37574cd4 100644
--- a/imap_processing/tests/ialirt/unit/test_process_swapi.py
+++ b/imap_processing/tests/ialirt/unit/test_process_swapi.py
@@ -69,6 +69,28 @@ def sc_xarray_data(sc_packet_path):
     return sc_xarray_data
 
 
+@pytest.fixture
+def swapi_presweep_sc_xarray_data(swapi_presweep_sc_packet_path):
+    """Extract spacecraft packet for testing."""
+
+    packet_path, xtce_ialirt_path = swapi_presweep_sc_packet_path
+    sc_xarray_data = packet_file_to_datasets(
+        packet_path, xtce_ialirt_path, use_derived_value=False
+    )[478]
+    return sc_xarray_data
+
+
+@pytest.fixture
+def swapi_postsweep_sc_xarray_data(swapi_postsweep_sc_packet_path):
+    """Extract spacecraft packet for testing."""
+
+    packet_path, xtce_ialirt_path = swapi_postsweep_sc_packet_path
+    sc_xarray_data = packet_file_to_datasets(
+        packet_path, xtce_ialirt_path, use_derived_value=False
+    )[478]
+    return sc_xarray_data
+
+
 @pytest.fixture
 def ialirt_test_data():
     """Extract test data for unit tests below."""
@@ -137,11 +159,14 @@ def test_process_swapi_ialirt(
         0 : xarray_data["swapi_flag"].shape[0]
     ].data
 
-    energy_passbands = pd.read_csv(
+    calibration_lut_table = pd.read_csv(
         f"{imap_module_directory}/tests/ialirt/data/l0/swapi_ialirt_energy_steps.csv"
     )
+    
+    # it is 0 in the template data, but there is no #0 in the table
+    xarray_data['swapi_version'] = (['epoch'], [calibration_lut_table['Sweep #'].max()] * len(xarray_data.epoch))
 
-    swapi_result = process_swapi_ialirt(xarray_data, energy_passbands)
+    swapi_result = process_swapi_ialirt(xarray_data, calibration_lut_table)
 
     key_names = [
         "apid",
@@ -172,11 +197,10 @@ def test_count_rate():
     )
 
 
-def test_optimize_parameters():
-    """Test that the optimize_pseudo_parameters() function works correctly."""
-
+@pytest.fixture
+def test_data():
     # The following files and values are all validation sets provided by the SWAPI team.
-    test_data = {
+    return {
         "test_set_1": {
             "file_name": "ialirt_test_data_u_sw_550_n_sw_5_T_sw_100000_v2.csv",
             "expected_values": {  # expected output and acceptable tolerance
@@ -203,70 +227,115 @@ def test_optimize_parameters():
         },
     }
 
+
+@pytest.fixture
+def energy_passbands():
     calibration_test_file = pd.read_csv(
         f"{imap_module_directory}/tests/ialirt/data/l0/swapi_ialirt_energy_steps.csv"
     )
     energy_passbands = calibration_test_file["Energy"][0:63].to_numpy().astype(float)
+    return energy_passbands
+
+
+def test_optimize_parameters(test_data, energy_passbands):
+    """Test that the optimize_pseudo_parameters() function works correctly."""
+
+    for test_set in test_data:
+        energy_data = pd.read_csv(
+            f"{imap_module_directory}/tests/ialirt/data/l0/"
+            f"{test_data[test_set]['file_name']}",
+        )
+        count_rates = energy_data["Count Rates [Hz]"].to_numpy()
+        count_rates[0] = 0.0  # set the first count to zero
+        count_rates_errors = energy_data["Count Rates Error [Hz]"].to_numpy()
+        
+        # select the first 63 only
+        count_rates = count_rates[:63]
+        count_rates_errors = count_rates_errors[:63]  
+
+        result = optimize_pseudo_parameters(
+            count_rates, count_rates_errors, energy_passbands
+        )
+
+        for param in test_data[test_set]["expected_values"]:
+            np.testing.assert_allclose(
+                result[param],
+                test_data[test_set]["expected_values"][param][0],
+                rtol=test_data[test_set]["expected_values"][param][1],
+                err_msg=f"{param} did not match the expected result "
+                f"within the tolerance.",
+            )
 
+
+def test_optimize_parameters_with_zero_count(test_data, energy_passbands):
+    """Test that the optimize_pseudo_parameters() function works correctly
+    with invalid count and count rate values."""
     for test_set in test_data:
         energy_data = pd.read_csv(
             f"{imap_module_directory}/tests/ialirt/data/l0/"
             f"{test_data[test_set]['file_name']}",
         )
         count_rates = energy_data["Count Rates [Hz]"].to_numpy()
-        count_rates[0] = 0.0
-        count_rates = np.tile(count_rates, (2, 1))
+        count_rates[0] = 0.0  # set the first count to zero
+        # so that there are N sweeps
         count_rates_errors = energy_data["Count Rates Error [Hz]"].to_numpy()
-        count_rates_errors = np.tile(count_rates_errors, (2, 1))
+        
+        # select the first 63 only
+        count_rates = count_rates[:63]
+        count_rates_errors = count_rates_errors[:63]  
+
+        # set some points to have invalid values that should not affect the fit too much
+        i_peak = count_rates.argmax()  # invalid values need to be close to the maximum to be detected
+
+        # zero count/count rate
+        count_rates[i_peak + 2] = 0
+        count_rates_errors[i_peak + 2] = 0
 
         result = optimize_pseudo_parameters(
             count_rates, count_rates_errors, energy_passbands
         )
 
         for param in test_data[test_set]["expected_values"]:
-            (
-                np.testing.assert_allclose(
-                    result[param][0],
-                    test_data[test_set]["expected_values"][param][0],
-                    rtol=test_data[test_set]["expected_values"][param][1],
-                ),
-                f"{param} did not match the expected result within the tolerance.",
+            # because invalid values inserted, higher error tolerance by 50%
+            np.testing.assert_allclose(
+                result[param],
+                test_data[test_set]["expected_values"][param][0],
+                rtol=0.5 + test_data[test_set]["expected_values"][param][1],
+                err_msg=f"{param} did not match the expected result "
+                f"within the tolerance.",
             )
 
 
 @pytest.mark.external_test_data
-def test_process_spacecraft_packet(sc_xarray_data):
-    """Tests spacecraft packet processing."""
+def test_process_presweep_spacecraft_packet(swapi_presweep_sc_xarray_data):
+    """Tests spacecraft packet processing presweep."""
     calibration_file = pd.read_csv(
-        f"{imap_module_directory}/tests/ialirt/data/l0/swapi_ialirt_energy_steps.csv"
+        f"{imap_module_directory}/tests/ialirt/data/l0/"
+        f"imap_swapi_esa-unit-conversion_20250626_v001.csv"
     )
 
-    # Case 1: Not fixing the sequence number attribute, which is all zeros.
-    swapi_product = process_swapi_ialirt(sc_xarray_data, calibration_file)
-    assert swapi_product == []
+    swapi_product = process_swapi_ialirt(
+        swapi_presweep_sc_xarray_data, calibration_file
+    )
 
-    # Case 2: Overwriting swapi_seq_number to be an acceptable array of numbers.
-    # Calculate how many times to tile the sequence to reach length of sc packet
-    target_length = sc_xarray_data["swapi_seq_number"].shape[0]
-    base_sequence = np.arange(12)
-    repeat_times = (target_length // len(base_sequence)) + 1  # Over-repeat
+    assert isinstance(swapi_product, list)
+    assert len(swapi_product) == 4
+    
+    assert np.isclose(float(swapi_product[0]['swapi_pseudo_proton_speed']), 450, rtol=0.1)
 
-    # Tile the sequence and truncate to target_length
-    extended_data = np.tile(base_sequence, repeat_times)[:target_length]
-    sc_xarray_data["swapi_seq_number"].data = extended_data
+@pytest.mark.external_test_data
+def test_process_postsweep_spacecraft_packet(swapi_postsweep_sc_xarray_data):
+    """Tests spacecraft packet processing postsweep."""
+    calibration_file = pd.read_csv(
+        f"{imap_module_directory}/tests/ialirt/data/l0/"
+        f"imap_swapi_esa-unit-conversion_20251201_v001.csv"
+    )
 
-    swapi_product1 = process_swapi_ialirt(sc_xarray_data, calibration_file)
-    key_names = [
-        "apid",
-        "met",
-        "met_in_utc",
-        "ttj2000ns",
-        "swapi_pseudo_proton_density",
-        "swapi_pseudo_proton_speed",
-        "swapi_pseudo_proton_temperature",
-    ]
+    swapi_product = process_swapi_ialirt(
+        swapi_postsweep_sc_xarray_data, calibration_file
+    )
 
-    for key in key_names:
-        assert swapi_product1[0][key] is not None, (
-            f"The expected attribute {key} was not filled in the result dict."
-        )
+    assert isinstance(swapi_product, list)
+    assert len(swapi_product) == 4
+    
+    assert np.isclose(float(swapi_product[0]['swapi_pseudo_proton_speed']), 400, rtol=0.1)
\ No newline at end of file