diff --git a/scripts/integral_batch_file.py b/scripts/integral_batch_file.py
index 8af81ece..bb59e7e5 100755
--- a/scripts/integral_batch_file.py
+++ b/scripts/integral_batch_file.py
@@ -7,10 +7,12 @@
 from datetime import datetime
 import chardet
 
+
 def show_usage():
     print("usage: column2integrate input_data_file output_data_file csv_delimiter decimal\n\
     e.g. python ./integral_batch_file.py \' Irradiation\' dades_in.csv dades_out.csv ';' ','")
 
+
 def trapezoidal_approximation(ordered_sensors, from_date, to_date, outputDataFormat='%Y-%m-%d %H:%M:%S%z', timeSpacing=5./60., column2integrateTitle=0):
     ''' Trapezoidal aproximation of the 24 hours following first sensor entry'''
 
@@ -36,16 +38,19 @@ def trapezoidal_approximation(ordered_sensors, from_date, to_date, outputDataFor
         integrals.append((timeString, test_integral))
     return integrals
 
+
 def to_ECT_csv(integrals):
     '''TODO handle tz-aware output instead ot delivering UTC and better UTC-sandwich'''
     return
 
+
 def asciigraph_print(tuple_array, scale_factor = 10):
     '''for the lulz'''
 
     for t, v in tuple_array:
         print("{} {} {:.2f}".format(t, v if pd.isna(v) else '=' * int(v/scale_factor), v) )
 
+
 def find_encoding(fname):
     with open(fname, 'rb') as f:
         r_file = f.read()
@@ -53,6 +58,7 @@ def find_encoding(fname):
     charenc = result['encoding']
     return charenc
 
+
 def parse_csv(input_data_file, column2integrate, delimiter, decimal):
 
     guessed_encoding = find_encoding(input_data_file)
@@ -78,6 +84,7 @@ def parse_csv(input_data_file, column2integrate, delimiter, decimal):
 
     return sensors
 
+
 def parse_xlsx(input_data_file, column2integrate):
 
     columnNames = pd.read_excel(io=input_data_file, header=0).columns
@@ -94,6 +101,14 @@ def parse_xlsx(input_data_file, column2integrate):
 
     return ordered_sensors
 
+
+def dropNonMonotonicRows(df):
+    ''' strictly monotonic increasing '''
+    anomalies = [r[1] for r in zip(df.index, df.index[1:]) if r[0] >= r[1]]
+    df.drop(index=anomalies, inplace=True)
+    return anomalies
+
+
 def main():
 
     if len(sys.argv[1:]) != 5:
@@ -136,6 +151,11 @@ def main():
     from_date = ordered_sensors.index[0].floor('d')
     to_date   = ordered_sensors.index[-1].ceil('d')
 
+    if not ordered_sensors.index.is_monotonic_increasing:
+        print("Index is NOT monotonic! Attempting automatic fix")
+        anomalies = dropNonMonotonicRows(ordered_sensors)
+        print("\n[WARNING] Dropped rows with dates: {}".format(anomalies))
+
     integrals = trapezoidal_approximation(ordered_sensors, from_date, to_date, outputDataFormat, timeSpacing, columnTitle)
 
     integralsDF = pd.DataFrame(data = integrals, columns = ['datetime', columnTitle])
@@ -143,7 +163,12 @@ def main():
 
     print("Saved {} records from {} to {}".format(len(integralsDF), from_date, to_date))
     asciigraph_print(integrals)
+
+    if anomalies:
+        print("\n[WARNING] Dropped rows with dates: {}".format(anomalies))
+
     print("\nJob's done, have a good day\n")
 
+
 if __name__ == "__main__":
-	main()
+    main()