diff --git a/data/external/daysegments_bluetooth.csv b/data/external/daysegments_bluetooth.csv
index 2ec968b3..7ca74747 100644
--- a/data/external/daysegments_bluetooth.csv
+++ b/data/external/daysegments_bluetooth.csv
@@ -1,4 +1,4 @@
 label,start,end
 daily,00:00, 23:59
 morning,06:00, 11:59
-eveningblue3,18:00, 21:59
+eveningblue,18:00, 21:59
diff --git a/src/data/compute_day_segments.py b/src/data/compute_day_segments.py
index 874d7f59..4a149838 100644
--- a/src/data/compute_day_segments.py
+++ b/src/data/compute_day_segments.py
@@ -1,19 +1,43 @@
 import pandas as pd
 
+def is_valid_frequency_segments(day_segments):
+    return False
 
-def parse_day_segments(day_segments):
-    # Add code to parse frequencies, intervals, and events
-    # Expected formats:
-    # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int)
-    # Interval: label, start, end columns (e.g. daily, 00:00, 23:59) start and end should be valid hours in 24 hour format
-    # Event: label, timestamp, length, shift (e.g., survey1, 1532313215463, 60, -30), timestamp is a UNIX timestamp in ms, length is in minutes (int), shift is in minutes (+/-int) and is add/substracted from timestamp
-    # Our output should have local_date, start_time, end_time, label. In the readable_datetime script, If local_date has the same value for all rows, every segment will be applied for all days, otherwise each segment will be applied only to its local_date
-    
+def is_valid_interval_segments(day_segments):
+    return True
+
+def is_valid_event_segments(day_segments):
+    return False
+
+def parse_frequency_segments(day_segments):
+    return day_segments
+
+def parse_interval_segments(day_segments):
     day_segments["local_date"] = 1
     day_segments = day_segments.rename(columns={"start": "start_time", "end":"end_time"})
     return day_segments
-    ##########################
 
-day_segments = pd.read_csv(snakemake.input[0])
-day_segments = parse_day_segments(day_segments)
+def parse_event_segments(day_segments):
+    return day_segments
+
+def parse_day_segments(day_segments_file):
+    # Add code to validate and parse frequencies, intervals, and events
+    # Expected formats:
+    # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int)
+    # Interval: label, start, end columns (e.g. daily, 00:00, 23:59) start and end should be valid hours in 24 hour format
+    # Event: label, timestamp, length, shift (e.g., survey1, 1532313215463, 60, -30), timestamp is a UNIX timestamp in ms (we could take a date time string instead), length is in minutes (int), shift is in minutes (+/-int) and is added/substracted from timestamp
+    # Our output should have local_date, start_time, end_time, label. In the readable_datetime script, If local_date has the same value for all rows, every segment will be applied for all days, otherwise each segment will be applied only to its local_date
+    day_segments = pd.read_csv(day_segments_file)
+
+    if(is_valid_frequency_segments(day_segments)):
+        day_segments = parse_frequency_segments(day_segments)
+    elif(is_valid_interval_segments(day_segments)):
+        day_segments = parse_interval_segments(day_segments)
+    elif(is_valid_event_segments(day_segments)):
+        day_segments = parse_event_segments(day_segments)
+    else:
+        raise ValueError("{} does not have a format compatible with frequency, interval or event day segments. Please refer to [LINK]".format(day_segments_file))
+    return day_segments
+
+day_segments = parse_day_segments(snakemake.input[0])
 day_segments.to_csv(snakemake.output["segments_file"], index=False)
\ No newline at end of file