DOC: Rename 'guid' to 'id' in dividend tracking logic.

zipline/finance/performance/period.py

@@ -232,7 +232,7 @@ def _maybe_pay_dividend(self, dividend):
         with fields drawn from zipline.protocol.DIVIDEND_PAYMENT_FIELDS.
         """
         try:
-            unpaid_dividend = self._unpaid_dividends.loc[dividend['guid']]
+            unpaid_dividend = self._unpaid_dividends.loc[dividend['id']]
             return unpaid_dividend
         except KeyError:
             return zp.dividend_payment()

zipline/finance/performance/position.py

@@ -61,7 +61,7 @@ def earn_dividend(self, dividend):
         that we can pay out the correct amount on the dividend's pay date.
         """
         assert dividend['sid'] == self.sid
-        out = {'guid': dividend['guid']}
+        out = {'id': dividend['id']}
 
         # stock dividend
         if dividend['payment_sid']:

zipline/finance/performance/tracker.py

@@ -194,20 +194,23 @@ def set_date(self, date):
 
     def update_dividends(self, new_dividends):
         """
-        Update our dividend frame with new dividends.
+        Update our dividend frame with new dividends.  @new_dividends should be
+        a DataFrame with columns containing at least the entries in
+        zipline.protocol.DIVIDEND_FIELDS.
         """
+
         # Mark each new dividend with a unique integer id.  This ensures that
         # we can differentiate dividends whose date/sid fields are otherwise
         # identical.
-        new_dividends['guid'] = np.arange(
+        new_dividends['id'] = np.arange(
             self._dividend_count,
             self._dividend_count + len(new_dividends),
         )
         self._dividend_count += len(new_dividends)
 
         self.dividend_frame = pd.concat(
             [self.dividend_frame, new_dividends]
-        ).sort(['pay_date', 'ex_date']).set_index('guid', drop=False)
+        ).sort(['pay_date', 'ex_date']).set_index('id', drop=False)
 
     def update_performance(self):
         # calculate performance as of last trade

zipline/protocol.py

@@ -47,34 +47,33 @@
     'sid',
 ]
 # Expected fields/index values for a dividend payment Series.
-DIVIDEND_PAYMENT_FIELDS = ['guid', 'payment_sid', 'cash_amount', 'share_count']
+DIVIDEND_PAYMENT_FIELDS = ['id', 'payment_sid', 'cash_amount', 'share_count']
 
 
 def dividend_payment(data=None):
     """
     Take a dictionary whose values are in DIVIDEND_PAYMENT_FIELDS and return a
     series representing the payment of a dividend.
 
-    Guids are assigned to each historical dividend in
+    Ids are assigned to each historical dividend in
     PerformanceTracker.update_dividends. They are guaranteed to be unique
     integers with the context of a single simulation. If @data is non-empty, a
-    guid is required to identify the historical dividend associated with this
+    id is required to identify the historical dividend associated with this
     payment.
 
     Additionally, if @data is non-empty, either data['cash_amount'] should be
     nonzero or data['payment_sid'] should be a security identifier and
     data['share_count'] should be nonzero.
 
-    The returned Series is given its guid value as a name so that concatenating
-    payments results in a DataFrame indexed by guid.  (Note, however, that the
+    The returned Series is given its id value as a name so that concatenating
+    payments results in a DataFrame indexed by id.  (Note, however, that the
     name value is not used to construct an index when this series is returned
-    by function called by `DataFrame.apply`.  In such a case, pandas preserves
+    by function passed to `DataFrame.apply`.  In such a case, pandas preserves
     the index of the DataFrame on which `apply` is being called.)
-
     """
     return pd.Series(
         data=data,
-        name=data['guid'] if data is not None else None,
+        name=data['id'] if data is not None else None,
         index=DIVIDEND_PAYMENT_FIELDS,
         dtype=object,
     )