drop pytimeparse requirement

use pandas timedelta as it offers comparable single item parsing
and better batch parsing

Change-Id: Ifd79385a4363ae4a9da5e9e94513ac98c7cdb1cb

gnocchi/aggregates/moving_stats.py

@@ -16,14 +16,13 @@
 import datetime
 
 import numpy
+from oslo_utils import strutils
+from oslo_utils import timeutils
 import pandas
 import six
 
 from gnocchi import aggregates
-
-from oslo_utils import strutils
-from oslo_utils import timeutils
-from pytimeparse import timeparse
+from gnocchi import utils
 
 
 class MovingAverage(aggregates.CustomAggregator):
@@ -35,7 +34,7 @@ class MovingAverage(aggregates.CustomAggregator):
             msg = 'Moving aggregate must have window specified.'
             raise aggregates.CustomAggFailure(msg)
         try:
-            return float(timeparse.timeparse(six.text_type(window)))
+            return utils.to_timespan(six.text_type(window)).total_seconds()
         except Exception:
             raise aggregates.CustomAggFailure('Invalid value for window')
 

gnocchi/tests/test_aggregates.py

@@ -46,14 +46,9 @@ class TestAggregates(tests_base.TestCase):
             self.assertEqual(60.0, result)
 
             window = '60'
-            self.assertRaises(aggregates.CustomAggFailure,
-                              agg_obj.check_window_valid,
-                              window)
-
-            window = None
-            self.assertRaises(aggregates.CustomAggFailure,
-                              agg_obj.check_window_valid,
-                              window)
+            agg_obj = self.custom_agg[agg_method]
+            result = agg_obj.check_window_valid(window)
+            self.assertEqual(60.0, result)
 
     def _test_create_metric_and_data(self, data, spacing):
         metric = storage.Metric(

gnocchi/utils.py

@@ -22,7 +22,6 @@ import iso8601
 import numpy
 from oslo_utils import timeutils
 import pandas as pd
-from pytimeparse import timeparse
 import six
 import tenacity
 import uuid
@@ -116,11 +115,9 @@ def to_timespan(value):
         seconds = float(value)
     except Exception:
         try:
-            seconds = timeparse.timeparse(six.text_type(value))
+            seconds = pd.Timedelta(six.text_type(value)).total_seconds()
         except Exception:
             raise ValueError("Unable to parse timespan")
-    if seconds is None:
-        raise ValueError("Unable to parse timespan")
     if seconds <= 0:
         raise ValueError("Timespan must be positive")
     return datetime.timedelta(seconds=seconds)

requirements.txt

@@ -9,7 +9,6 @@ oslo.utils>=3.18.0
 oslo.middleware>=3.11.0
 pandas>=0.17.0
 pecan>=0.9
-pytimeparse>=1.1.5
 futures
 jsonpatch
 cotyledon>=1.5.0