DMTF CADF format

Adding support for the DMTF Cloud Audit (CADF) format which will be
used along with a generic notification filter to audit 'core'
component APIs.

initial code drop

blueprint support-standard-audit-formats

Change-Id: I3b27ceae8faa6427e4be1290c1406102e790e2e3

1 files changed, 67 insertions, 0 deletions

diff --git a/pycadf/measurement.py b/pycadf/measurement.py
new file mode 100644
index 0000000..58bd03b
--- /dev/null
+++ b/pycadf/measurement.py
@@ -0,0 +1,67 @@
+# -*- encoding: utf-8 -*-
+#
+# Copyright 2013 IBM Corp.
+#
+# Author: Matt Rutkowski <mrutkows@us.ibm.com>
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may
+# not use this file except in compliance with the License. You may obtain
+# a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations
+# under the License.
+
+from pycadf import cadftype
+from pycadf import identifier
+from pycadf import metric
+from pycadf import resource
+
+MEASUREMENT_KEYNAME_RESULT = "result"
+MEASUREMENT_KEYNAME_METRIC = "metric"
+MEASUREMENT_KEYNAME_METRICID = "metricId"
+MEASUREMENT_KEYNAME_CALCBY = "calculatedBy"
+
+MEASUREMENT_KEYNAMES = [MEASUREMENT_KEYNAME_RESULT,
+                        MEASUREMENT_KEYNAME_METRICID,
+                        MEASUREMENT_KEYNAME_METRIC,
+                        MEASUREMENT_KEYNAME_CALCBY]
+
+
+class Measurement(cadftype.CADFAbstractType):
+
+    result = cadftype.ValidatorDescriptor(MEASUREMENT_KEYNAME_RESULT)
+    metric = cadftype.ValidatorDescriptor(
+        MEASUREMENT_KEYNAME_METRIC, lambda x: isinstance(x, metric.Metric))
+    metricId = cadftype.ValidatorDescriptor(MEASUREMENT_KEYNAME_METRICID,
+                                            lambda x: identifier.is_valid(x))
+    calculatedBy = cadftype.ValidatorDescriptor(
+        MEASUREMENT_KEYNAME_CALCBY,
+        (lambda x: isinstance(x, resource.Resource) and x.is_valid()))
+
+    def __init__(self, result=None, metric=None, metricId=None,
+                 calculatedBy=None):
+
+        # Measurement.result
+        if result is not None:
+            setattr(self, MEASUREMENT_KEYNAME_RESULT, result)
+
+        # Measurement.metricId
+        if metricId is not None:
+            setattr(self, MEASUREMENT_KEYNAME_METRICID, metricId)
+
+        # Measurement.metric
+        if metric is not None:
+            setattr(self, MEASUREMENT_KEYNAME_METRIC, metric)
+
+        # Measurement.calculaedBy
+        if calculatedBy is not None:
+            setattr(self, MEASUREMENT_KEYNAME_CALCBY, calculatedBy)
+
+    # self validate this cadf:Measurement type against schema
+    def is_valid(self):
+        return hasattr(self, MEASUREMENT_KEYNAME_RESULT)