#    Copyright 2013-2015 ARM Limited
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
#

import re

from wlauto import AndroidUiAutoBenchmark

[docs]class Caffeinemark(AndroidUiAutoBenchmark):

name = 'caffeinemark'
description = """
CaffeineMark is a series of tests that measure the speed of Java
programs running in various hardware and software configurations.

http://www.benchmarkhq.ru/cm30/info.html

From the website:

CaffeineMark scores roughly correlate with the number of Java instructions
executed per second, and do not depend significantly on the the amount of
memory in the system or on the speed of a computers disk drives or internet
connection.

The following is a brief description of what each test does:

- Sieve: The classic sieve of eratosthenes finds prime numbers.
- Loop: The loop test uses sorting and sequence generation as to measure
compiler optimization of loops.
- Logic: Tests the speed with which the virtual machine executes
decision-making instructions.
- Method: The Method test executes recursive function calls to see how
well the VM handles method calls.
- Float: Simulates a 3D rotation of objects around a point.
- Graphics: Draws random rectangles and lines.
- Image: Draws a sequence of three graphics repeatedly.
- Dialog: Writes a set of values into labels and editboxes on a form.

The overall CaffeineMark score is the geometric mean of the individual
scores, i.e., it is the 9th root of the product of all the scores.
"""
package = "com.flexycore.caffeinemark"
activity = ".Application"
summary_metrics = ['OverallScore']

regex = re.compile(r'CAFFEINEMARK RESULT: (?P<type>\w+) (?P<value>\S+)')

[docs]    def update_result(self, context):
super(Caffeinemark, self).update_result(context)
with open(self.logcat_log) as fh:
for line in fh:
match = self.regex.search(line)
if match:
metric = match.group('type')
value = float(match.group('value'))