swe update

imap_processing/swe/l1a/swe_science.py

@@ -112,7 +112,7 @@ def swe_science(decom_data: list, data_version: str) -> xr.Dataset:
     science_array = []
     raw_science_array = []
 
-    metadata_arrays: np.array = collections.defaultdict(list)
+    metadata_arrays: dict = collections.defaultdict(list)
 
     # We know we can only have 8 bit numbers input, so iterate over all
     # possibilities once up front
@@ -133,7 +133,8 @@ def swe_science(decom_data: list, data_version: str) -> xr.Dataset:
         # where 1260 = 180 x 7 CEMs
         # Take the "raw_counts" indices/counts mapping from
         # decompression_table and then reshape the return
-        uncompress_data = np.take(decompression_table, raw_counts).reshape(180, 7)
+        uncompress_data_ndarray: np.ndarray = np.take(decompression_table, raw_counts)
+        uncompress_data = uncompress_data_ndarray.reshape(180, 7)
         # Save raw counts data as well
         raw_counts = raw_counts.reshape(180, 7)
 

imap_processing/swe/l1b/swe_l1b_science.py

@@ -114,7 +114,7 @@ def deadtime_correction(counts: np.ndarray, acq_duration: int) -> np.ndarray:
     deadtime = 1.5e-6
     correct = 1.0 - (deadtime * counts / acq_duration)
     correct = np.maximum(0.1, correct)
-    corrected_count = np.divide(counts, correct)
+    corrected_count: np.ndarray = np.divide(counts, correct)
     return corrected_count
 
 
@@ -139,7 +139,8 @@ def convert_counts_to_rate(data: np.ndarray, acq_duration: int) -> np.ndarray:
     # convert milliseconds to seconds
     # Todo: check with SWE team about int or float types.
     acq_duration = int(acq_duration / 1000.0)
-    return data / acq_duration
+    count_rates: np.ndarray = data / acq_duration
+    return count_rates
 
 
 def calculate_calibration_factor(time: int) -> None:
@@ -231,7 +232,7 @@ def populate_full_cycle_data(
     # in odd column every six steps.
     if esa_table_num == 0:
         # create new full cycle data array
-        full_cycle_data = np.zeros((24, 30, 7))
+        full_cycle_data: np.ndarray = np.zeros((24, 30, 7))
 
         # Initialize esa_step_number and column_index.
         # esa_step_number goes from 0 to 719 range where
@@ -294,7 +295,8 @@ def find_cycle_starts(cycles: np.ndarray) -> np.ndarray:
         Array of indices of start cycle.
     """
     if cycles.size < 4:
-        return np.array([], np.int64)
+        start_cycle_: np.ndarray = np.array([], np.int64)
+        return start_cycle_
 
     # calculate difference between consecutive cycles
     diff = cycles[1:] - cycles[:-1]
@@ -309,7 +311,8 @@ def find_cycle_starts(cycles: np.ndarray) -> np.ndarray:
     # [0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0]      # And all?
     ione = diff == 1
     valid = (cycles == 0)[:-3] & ione[:-2] & ione[1:-1] & ione[2:]
-    return np.where(valid)[0]
+    start_cycle: np.ndarray = np.where(valid)[0]
+    return start_cycle
 
 
 def get_indices_of_full_cycles(quarter_cycle: np.ndarray) -> np.ndarray:
@@ -333,7 +336,9 @@ def get_indices_of_full_cycles(quarter_cycle: np.ndarray) -> np.ndarray:
     #   Eg. [[0, 1, 2, 3]]
     # then we add both of them together to get an array of shape(n, 4)
     #   Eg. [[3, 4, 5, 6], [8, 9, 10, 11]]
-    full_cycles_indices = indices_of_start[..., None] + np.arange(4)[None, ...]
+    full_cycles_indices: np.ndarray = (
+        indices_of_start[..., None] + np.arange(4)[None, ...]
+    )
     return full_cycles_indices.reshape(-1)