Cleaned up main body.

Author: fnunnari

PostProcessing/dataframes.py

@@ -1,9 +1,11 @@
 import pandas as pd
 
+from typing import Tuple
 
 def repair_dropped_frames(df: pd.DataFrame) -> pd.DataFrame:
     pass
 
+
 # Function to find the largest value in the first entry of all dataframes
 def find_largest_first_entry(dfs):
     largest_value = float('-inf')
@@ -13,6 +15,7 @@ def find_largest_first_entry(dfs):
             largest_value = first_entry
     return largest_value
 
+
 # Function to find the smallest value in the last entry of selected dataframes
 def find_smallest_last_entry(dfs):
     smallest_value = float('inf')
@@ -22,22 +25,24 @@ def find_smallest_last_entry(dfs):
             smallest_value = last_entry
     return smallest_value
 
+
 # Function to find the largest & smallest value in the first and last entry of dataframes
-def compute_time_range(dfs):
+def compute_time_range(dfs) -> Tuple[int, int]:
     # Find the lowest and highest numbers in all the data frames
     lower_value = find_largest_first_entry(dfs)
     higher_value = find_smallest_last_entry(dfs)
 
     # return the results
-    return (lower_value, higher_value)
+    return lower_value, higher_value
+
 
 # Function to trim dataframes based on specified values
 def trim_into_interval(dfs, min_common, max_common, threshold):
     trimmed_dataframes = []
     # import pdb;pdb.set_trace()
     for df in dfs:
-        start = df[(df.iloc[:, 0] >= min_common - threshold) & (df.iloc[:, 0] <= min_common + threshold)]
-        end = df[(df.iloc[:, 0] >= max_common - threshold) & (df.iloc[:, 0] <= max_common + threshold)]
+        start: pd.DataFrame = df[(df.iloc[:, 0] >= min_common - threshold) & (df.iloc[:, 0] <= min_common + threshold)]
+        end: pd.DataFrame = df[(df.iloc[:, 0] >= max_common - threshold) & (df.iloc[:, 0] <= max_common + threshold)]
         trimmed_df = df[(df.iloc[:, 0] >= start.iloc[0, 0]) & (df.iloc[:, 0] <= end.iloc[0, 0])].reset_index(drop=True)
         trimmed_dataframes.append(trimmed_df)
     return trimmed_dataframes