Command line interface and timeline interval improvements.

booksoup/BookSoup.py

@@ -45,7 +45,7 @@ def load_conversation(self, search_name, interval="month"):
             for link in convo_links:
                 if link.text != search_name:
                     continue
-                contact = Conversation(os.path.join(self.__path, link["href"]))
+                contact = Conversation(os.path.join(self.__path, link["href"]), interval=interval)
                 self.conversations[contact.name] = contact
                 return contact
         return None

booksoup/Conversation.py

@@ -30,12 +30,15 @@ def interaction_freq(self):
     def interaction_timeline(self, name):
         return self.__fbt.interaction_timeline(name, self.messages)
 
-    def sentiment_timeline(self, name):
-        return self.__sent.sentiment_timeline(name)
+    def sentiment_timeline(self, name, interval):
+        return self.__sent.sentiment_timeline(name, interval)
 
     def avg_sentiment(self, name):
         return self.__sent.avg_sentiment(name)
 
+    def get24HourTime(self, elem):
+        return self.__fbt.get24HourTime(elem)
+
     # Returns a list of participants in the conversation.
     def __scrape_participants(self):
         users = []

booksoup/FbTime.py

@@ -17,8 +17,12 @@ def interaction_freq(self):
 
         for date_str in self.span_meta:
             time = date_str.split("at ")[1][:5]
+            amOrPm = date_str.split(":")[1][2:4]
             hour = time.split(":")[0]
-            times[hour+":00"] += 1
+            if len(hour) == 1:
+                times["0"+hour+":00"+amOrPm] += 1
+            else:
+                times[hour+":00"+amOrPm] += 1
         return times
 
     # Returns a dict where each key is a date and each value is the number of
@@ -33,8 +37,11 @@ def interaction_timeline(self, name, messages):
     # Creates a dictionary of times on the hour where each value is 0.
     def generate_time_dict(self):
         times = {}
-        for h in range(0,24):
-            time = self.__pad(h) + ":" + "00"
+        for h in range(1,13):
+            time = self.__pad(h) + ":" + "00am"
+            times[time] = 0
+        for h in range(1,13):
+            time = self.__pad(h) + ":" + "00pm"
             times[time] = 0
         return times
 
@@ -80,7 +87,19 @@ def span_meta_to_date(self, span_str, interval="month"):
             span_str = ''.join(span_str.rsplit(',', 1))
 
         date_arr = span_str.split(", ")[1].split(" ")[:3]
-        date_str = date_arr[2]+"-"+self.__pad(list(calendar.month_name).index(date_arr[1]))
+        date_str = date_arr[2]+"-"+self.__pad(list(calendar.month_name).index(date_arr[0]))
         if interval == "day":
-            date_str += "-"+self.__pad(date_arr[0])
+            date_str += "-"+self.__pad(date_arr[1])
         return date_str
+
+    def get24HourTime(self, elem):
+        amOrPm = elem.split(":")[1][2:4]
+        hour = int(elem.split(":")[0])
+        if amOrPm == "am":
+            if hour == 12:
+                return hour+12
+            return hour
+        else:
+            if hour == 12:
+                return hour
+            return hour+12

booksoup/Sentiment.py

@@ -11,9 +11,9 @@ def __init__(self, messages, fbt):
         self.fbt = fbt
         self.messages = messages
 
-    def sentiment_timeline(self, name):
-        timeline = self.fbt.generate_date_dict()
-        sentiment_counts = self.fbt.generate_date_dict()
+    def sentiment_timeline(self, name, interval="month"):
+        timeline = self.fbt.generate_date_dict(interval)
+        sentiment_counts = self.fbt.generate_date_dict(interval)
         for message in self.messages:
             if message.content is None or message.name != name:
                 continue

demo_interaction_frequency.py

@@ -3,16 +3,17 @@
 from booksoup import BookSoup
 import numpy as np
 import matplotlib.pyplot as plt
+import sys
 
 # Enter the path to the top level of your facebook data folder below.
-me = BookSoup("facebook-data")
+me = BookSoup(sys.argv[2])
 
 # Enter the name of the conversation or the numerical ID below.
-contact = me.load_conversation(274)
+contact = me.load_conversation(sys.argv[1], sys.argv[3])
 
 times = contact.interaction_freq()
 
-objects = sorted(times.keys())
+objects = sorted(times.keys(), key=contact.get24HourTime)
 y_pos = np.arange(len(objects))
 vals = [times[t] for t in objects]
 

demo_interaction_timeline.py

@@ -3,16 +3,17 @@
 from booksoup import BookSoup
 import numpy as np
 import matplotlib.pyplot as plt
+import sys 
 
 times = []
 objects = []
 vals = []
 
 # Enter the path to the top level of your facebook data folder below.
-me = BookSoup("facebook-data")
+me = BookSoup(sys.argv[2])
 
 # Enter the name of the conversation or the numerical ID below.
-conversation = me.load_conversation(108)
+conversation = me.load_conversation(sys.argv[1])
 
 for participant in conversation.participants:
     timeline = conversation.interaction_timeline(participant)

demo_sentiment_timeline.py

@@ -3,19 +3,21 @@
 from booksoup import BookSoup
 import numpy as np
 import matplotlib.pyplot as plt
+import sys
 
 times = []
 objects = []
 vals = []
 
 # Enter the path to the top level of your facebook data folder below.
-me = BookSoup("facebook-data")
+me = BookSoup(sys.argv[2])
+interval = sys.argv[3]
 
 # Enter the name of the conversation or the numerical ID below.
-conversation = me.load_conversation(104)
+conversation = me.load_conversation(sys.argv[1], interval=interval)
 
 for participant in conversation.participants:
-    timeline = conversation.sentiment_timeline(participant)
+    timeline = conversation.sentiment_timeline(participant, interval)
     sorted_keys = sorted(timeline.keys())
     times.append(timeline)
     objects.append(sorted_keys)