added luts

imap_processing/tests/ultra/unit/test_lookup_utils.py

@@ -10,13 +10,13 @@
     get_y_adjust,
 )
 
-base_path = f"{imap_module_directory}/ultra/lookup_tables"
+base_path = imap_module_directory / "ultra" / "lookup_tables"
 
 
 def test_get_y_adjust():
     """Tests function get_y_adjust."""
 
-    yadjust_path = f"{base_path}/yadjust.csv"
+    yadjust_path = base_path / "yadjust.csv"
     yadjust_df = pd.read_csv(yadjust_path).set_index("dYLUT")
 
     array = np.array([8])
@@ -28,8 +28,8 @@ def test_get_y_adjust():
 def test_get_stop_norm():
     """Tests function get_stop_norm."""
 
-    tdc_norm_path = f"{base_path}/Ultra45_tdc_norm_LUT_IntPulser_20230901.csv"
-    tdc_norm_df = pd.read_csv(tdc_norm_path, header=0)
+    tdc_norm_path = base_path / "ultra45_tdc_norm.csv"
+    tdc_norm_df = pd.read_csv(tdc_norm_path, header=1)
 
     array = np.array([378])
     stop_norm = get_norm(array, "SpE", "ultra45")
@@ -40,7 +40,7 @@ def test_get_stop_norm():
 def test_get_back_position():
     """Tests function get_back_position."""
 
-    back_pos_path = f"{base_path}/back-pos-luts_SN202_20230216.csv"
+    back_pos_path = base_path / "ultra45_back-pos-luts.csv"
     back_pos_df = pd.read_csv(back_pos_path, index_col="Index_offset")
 
     array = np.array([-2000])
@@ -52,7 +52,7 @@ def test_get_back_position():
 def test_get_egy_norm():
     """Tests function get_egy_norm."""
 
-    egy_norm_path = f"{base_path}/EgyNorm.mem.csv"
+    egy_norm_path = base_path / "EgyNorm.mem.csv"
     egy_norm_df = pd.read_csv(egy_norm_path)
 
     norm_composite_energy = get_energy_norm(np.array([2]), np.array([2]))
@@ -65,4 +65,4 @@ def test_get_image_params():
 
     image_params = get_image_params("XFTLTOFF")
 
-    assert image_params == 47.5
+    assert image_params == 49.3

imap_processing/tests/ultra/unit/test_ultra_l1b_extended.py

@@ -22,34 +22,16 @@ def test_get_front_x_position(
     events_fsw_comparison_theta_0,
 ):
     """Tests get_front_x_position function."""
-    # Left Start Type
-    indices_left = np.where(de_dataset["START_TYPE"] == 1)[0]
-    # Right Start Type
-    indices_right = np.where(de_dataset["START_TYPE"] == 2)[0]
 
     df = pd.read_csv(events_fsw_comparison_theta_0)
     df_filt = df[df["StartType"] != -1]
-    selected_rows_1 = df_filt.iloc[indices_left]
-    selected_rows_2 = df_filt.iloc[indices_right]
 
     xf = get_front_x_position(
         de_dataset["START_TYPE"].data,
         de_dataset["START_POS_TDC"].data,
     )
 
-    # TODO: Need new lookup tables for "XFTLTOFF", "XFTRTOFF"
-    # Need new lookup table.
-    # The value 180 was added to xf_1 since that
-    # is the offset from the FSW xft_off
-    assert np.allclose(
-        xf[indices_left] + 180, selected_rows_1.Xf.values.astype("float"), rtol=1e-3
-    )
-
-    # The value 25 was subtracted from xf_2 bc that
-    # is the offset from the FSW xft_off
-    assert np.allclose(
-        xf[indices_right] - 25, selected_rows_2.Xf.values.astype("float"), rtol=1e-3
-    )
+    assert np.allclose(xf, df_filt.Xf.values.astype("float"), rtol=1e-5)
 
 
 def test_xb_yb(
@@ -167,11 +149,11 @@ def test_get_ssd_offset_and_positions(
 
     # -4 is a value of an offset for SSD3 for Left Start Type and
     # SSD0 for Right Start Type.
-    offset_length = len(tof_offsets[tof_offsets == -4])
+    offset_length = len(tof_offsets[tof_offsets == -4.2])
     expected_offset_length = len(
         selected_rows[
             ((selected_rows["StartType"] == 1) & (selected_rows["SSDS3"] == 1))
-            | ((selected_rows["StartType"] == 2) & (selected_rows["SSDS4"] == 1))
+            | ((selected_rows["StartType"] == 2) & (selected_rows["SSDS5"] == 1))
         ]
     )
 

imap_processing/ultra/l1b/lookup_utils.py

@@ -34,7 +34,7 @@ def get_y_adjust(dy_lut: np.ndarray):
     return yadj.values
 
 
-def get_norm(dn: np.ndarray, key: str, sensor: str):
+def get_norm(dn: np.ndarray, key: str, file_label: str):
     """
     Correct mismatches between the stop Time to Digital Converters (TDCs).
 
@@ -53,7 +53,7 @@ def get_norm(dn: np.ndarray, key: str, sensor: str):
     key : str
         TpSpNNorm, TpSpSNorm, TpSpENorm, or TpSpWNorm.
         BtSpNNorm, BtSpSNorm, BtSpENorm, or BtSpWNorm.
-    sensor : str
+    file_label : str
         Instrument (ultra45 or ultra90).
 
     Returns
@@ -62,20 +62,15 @@ def get_norm(dn: np.ndarray, key: str, sensor: str):
         Normalized DNs.
     """
     # We only need the center string, i.e. SpN, SpS, SpE, SpW
-    if sensor == "ultra45":
-        file_label = "Ultra45_tdc_norm_LUT_IntPulser_20230901.csv"
-    else:
-        file_label = "Ultra90_tdc_norm_LUT_IntPulser_20230614.csv"
-
-    tdc_norm_path = base_path / file_label
-    tdc_norm_df = pd.read_csv(tdc_norm_path, header=0, index_col="Index")
+    tdc_norm_path = base_path / f"{file_label}_tdc_norm.csv"
+    tdc_norm_df = pd.read_csv(tdc_norm_path, header=1, index_col="Index")
 
     dn_norm = tdc_norm_df[key].iloc[dn]
 
     return dn_norm.values
 
 
-def get_back_position(back_index: np.ndarray, key: str, sensor: str):
+def get_back_position(back_index: np.ndarray, key: str, file_label: str):
     """
     Convert normalized TDC values using lookup tables.
 
@@ -92,20 +87,15 @@ def get_back_position(back_index: np.ndarray, key: str, sensor: str):
         SpSNorm - SpNNorm + 2047, or SpENorm - SpWNorm + 2047.
     key : str
         XBkTp, YBkTp, XBkBt, or YBkBt.
-    sensor : str
+    file_label : str
         Instrument (ultra45 or ultra90).
 
     Returns
     -------
     dn_converted : np.ndarray
         Converted DNs to Units of hundredths of a millimeter.
     """
-    if sensor == "ultra45":
-        file_label = "back-pos-luts_SN202_20230216.csv"
-    else:
-        file_label = "back-pos-luts_SN201_20230717.csv"
-
-    back_pos_path = base_path / file_label
+    back_pos_path = base_path / f"{file_label}_back-pos-luts.csv"
     back_pos_df = pd.read_csv(back_pos_path, index_col="Index_offset")
 
     dn_converted = back_pos_df[key].iloc[back_index]
@@ -161,7 +151,7 @@ def get_image_params(image: str):
     value : np.float64
         Image parameter value from the CSV file.
     """
-    csv_file_path = base_path / "FM45_Startup1_ULTRA_IMGPARAMS_20240207T134735_.csv"
+    csv_file_path = base_path / "FM45_Startup1_ULTRA_IMGPARAMS_20240719.csv"
     df = pd.read_csv(csv_file_path)
     value = df[image].iloc[0]
 

imap_processing/ultra/l1b/ultra_l1b_extended.py

@@ -149,8 +149,6 @@ def get_front_x_position(start_type: np.array, start_position_tdc: np.array):
     xft_off = np.where(start_type[indices] == 1, xft_lt_off, xft_rt_off)
 
     # Calculate xf and convert to hundredths of a millimeter
-    # Note FSW uses xft_off+1.8, but the lookup table uses xft_off
-    # Note FSW uses xft_off-.25, but the lookup table uses xft_off
     xf = (xftsc * -start_position_tdc[indices] + xft_off) * 100
 
     return xf

imap_processing/ultra/lookup_tables/FM45_Startup1_ULTRA_IMGPARAMS_20240719.csv

@@ -0,0 +1,7 @@
+SHCOARSE,XFTSC,XFTLTOFF,XFTRTOFF,TOFSC,TOFTPOFF,TOFBTOFF,XFTTOF,XCOINTPSC,XCOINTPOFF,XCOINBTSC,XCOINBTOFF,ETOFSC,ETOFTPOFF,ETOFBTOFF,TOFDIFFTPMIN,TOFDIFFTPMAX,TOFDIFFBTMIN,TOFDIFFBTMAX,ETOFMIN,ETOFMAX,ETOFSLOPE1,ETOFOFF1,ETOFSLOPE2,ETOFOFF2,SPTPPHOFF,SPBTPHOFF,YBKSSD0,YBKSSD1,YBKSSD2,YBKSSD3,YBKSSD4,YBKSSD5,YBKSSD6,YBKSSD7,TOFSSDSC,TOFSSDLTOFF0,TOFSSDLTOFF1,TOFSSDLTOFF2,TOFSSDLTOFF3,TOFSSDLTOFF4,TOFSSDLTOFF5,TOFSSDLTOFF6,TOFSSDLTOFF7,TOFSSDRTOFF0,TOFSSDRTOFF1,TOFSSDRTOFF2,TOFSSDRTOFF3,TOFSSDRTOFF4,TOFSSDRTOFF5,TOFSSDRTOFF6,TOFSSDRTOFF7,TOFSSDTOTOFF,PATHSTEEPTHRESH,PATHMEDIUMTHRESH
+,0.172998047,49.3,48.25,0.5,-528,-525,0.001831055,0.067929688,41.75,0.067929688,-39.79492188,0.1,-44.5,-44.5,,,,,,,,,,,,,29.3,37.3,7.1,15.1,-15.1,-7.1,-37.3,-29.3,0.196484375,-6,-7.3,-3.8,-4.2,-3.8,-3.7,-6.3,-5,-5,-6.3,-3.7,-3.8,-4,-4.2,-7.3,-6,5.9,,
+445027647,0.172998047,47.5,48.5,0.5,-528,-525,0.001831055,0.068486328,42.25,0.068486328,-39.75,0.1,-44.5,-44.5,22.6,26.6,22.6,26.6,-40,9,0.666699219,10,0.75,-5,540,540,29.3,37.300000000000004,7.1000000000000005,15.1,-15.1,-7.1,-37.3,-29.3,0.196484375,-6,-7,-3.5,-4,-3.8,-3.5,-6.8,-5.5,-5.5,-6.8,-3.5,-3.8,-4,-3.5,-7,-6,5.300000000000001,50,65
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,