MAINT: Use pandas for data cache file I/O

The compatibility between the two versions was made easier by
letting pandas handle the heavy lifting, so pass filenames to the
pandas serialization methods, instead of dealing doing the file
handling and reading/writing within the data module.

zipline/data/loader.py

@@ -62,7 +62,7 @@ INDEX_MAPPING = {
 }
 
 
-def get_datafile(name, mode='r'):
+def get_data_filepath(name):
     """
     Returns a handle to data file.
 
@@ -72,7 +72,7 @@ def get_datafile(name, mode='r'):
     if not os.path.exists(DATA_PATH):
         os.makedirs(DATA_PATH)
 
-    return open(os.path.join(DATA_PATH, name), mode)
+    return os.path.join(DATA_PATH, name)
 
 
 def get_cache_filepath(name):
@@ -102,9 +102,8 @@ def dump_treasury_curves(module='treasuries', filename='treasury_curves.csv'):
 
     curves = pd.DataFrame(tr_data).T
 
-    datafile = get_datafile(filename, mode='wb')
-    curves.to_csv(datafile)
-    datafile.close()
+    data_filepath = get_data_filepath(filename)
+    curves.to_csv(data_filepath)
 
     return curves
 
@@ -121,10 +120,9 @@ def dump_benchmarks(symbol):
         benchmark = (daily_return.date, daily_return.returns)
         benchmark_data.append(benchmark)
 
-    datafile = get_datafile(get_benchmark_filename(symbol), mode='wb')
+    data_filepath = get_data_filepath(get_benchmark_filename(symbol))
     benchmark_returns = pd.Series(dict(benchmark_data))
-    benchmark_returns.to_csv(datafile)
-    datafile.close()
+    benchmark_returns.to_csv(data_filepath)
 
 
 def update_benchmarks(symbol, last_date):
@@ -135,9 +133,8 @@ def update_benchmarks(symbol, last_date):
 
     Puts source benchmark into zipline.
     """
-    datafile = get_datafile(get_benchmark_filename(symbol), mode='rb')
+    datafile = get_data_filepath(get_benchmark_filename(symbol))
     saved_benchmarks = pd.Series.from_csv(datafile)
-    datafile.close()
 
     try:
         start = last_date + timedelta(days=1)
@@ -146,9 +143,8 @@ def update_benchmarks(symbol, last_date):
             benchmark = pd.Series({daily_return.date: daily_return.returns})
             saved_benchmarks = saved_benchmarks.append(benchmark)
 
-        datafile = get_datafile(get_benchmark_filename(symbol), mode='wb')
+        datafile = get_data_filepath(get_benchmark_filename(symbol))
         saved_benchmarks.to_csv(datafile)
-        datafile.close()
     except benchmarks.BenchmarkDataNotFoundError as exc:
         logger.warn(exc)
     return saved_benchmarks
@@ -159,19 +155,18 @@ def get_benchmark_filename(symbol):
 
 
 def load_market_data(bm_symbol='^GSPC'):
+    bm_filepath = get_data_filepath(get_benchmark_filename(bm_symbol))
     try:
-        fp_bm = get_datafile(get_benchmark_filename(bm_symbol), "rb")
-    except IOError:
+        saved_benchmarks = pd.Series.from_csv(bm_filepath)
+    except OSError:
         print("""
 data files aren't distributed with source.
 Fetching data from Yahoo Finance.
 """.strip())
         dump_benchmarks(bm_symbol)
-        fp_bm = get_datafile(get_benchmark_filename(bm_symbol), "rb")
+        saved_benchmarks = pd.Series.from_csv(bm_filepath)
 
-    saved_benchmarks = pd.Series.from_csv(fp_bm)
     saved_benchmarks = saved_benchmarks.tz_localize('UTC')
-    fp_bm.close()
 
     most_recent = pd.Timestamp('today', tz='UTC') - trading_day
     most_recent_index = trading_days.searchsorted(most_recent)
@@ -207,17 +202,16 @@ Fetching data from Yahoo Finance.
     module, filename, source = INDEX_MAPPING.get(
         bm_symbol, INDEX_MAPPING['^GSPC'])
 
+    tr_filepath = get_data_filepath(filename)
     try:
-        fp_tr = get_datafile(filename, "rb")
-    except IOError:
+        saved_curves = pd.DataFrame.from_csv(tr_filepath)
+    except OSError:
         print("""
 data files aren't distributed with source.
 Fetching data from {0}
 """.format(source).strip())
         dump_treasury_curves(module, filename)
-        fp_tr = get_datafile(filename, "rb")
-
-    saved_curves = pd.DataFrame.from_csv(fp_tr)
+        saved_curves = pd.DataFrame.from_csv(tr_filepath)
 
     # Find the offset of the last date for which we have trading data in our
     # list of valid trading days
@@ -238,8 +232,6 @@ Fetching data from {0}
         #                       tzinfo=pytz.utc)
         tr_curves[tr_dt] = curve.to_dict()
 
-    fp_tr.close()
-
     tr_curves = OrderedDict(sorted(
         ((dt, c) for dt, c in iteritems(tr_curves)),
         key=lambda t: t[0]))