wa: Rename results_processors to output_processors

For clarity and to better reflect their purpose, rename
`results_processors` to `output_processors`.

wa/output_processors/csvproc.py

@@ -0,0 +1,97 @@
+import csv
+
+from wa import OutputProcessor, Parameter
+from wa.framework.exception import ConfigError
+from wa.utils.types import list_of_strings
+
+
+class CsvReportProcessor(OutputProcessor):
+
+    name = 'csv'
+    description = """
+    Creates a ``results.csv`` in the output directory containing results for
+    all iterations in CSV format, each line containing a single metric.
+
+    """
+
+    parameters = [
+        Parameter('use_all_classifiers', kind=bool, default=False,
+                  global_alias='use_all_classifiers',
+                  description="""
+                  If set to ``True``, this will add a column for every classifier
+                  that features in at least one collected metric.
+
+                  .. note:: This cannot be ``True`` if ``extra_columns`` is set.
+
+                  """),
+        Parameter('extra_columns', kind=list_of_strings,
+                  description="""
+                  List of classifiers to use as columns.
+
+                   .. note:: This cannot be set if ``use_all_classifiers`` is
+                             ``True``.
+
+                  """),
+    ]
+
+    def validate(self):
+        super(CsvReportProcessor, self).validate()
+        if self.use_all_classifiers and self.extra_columns:
+            msg = 'extra_columns cannot be specified when '\
+                  'use_all_classifiers is True'
+            raise ConfigError(msg)
+
+    def initialize(self):
+        self.outputs_so_far = []  # pylint: disable=attribute-defined-outside-init
+        self.artifact_added = False
+
+    def process_job_output(self, output, target_info, run_output):
+        self.outputs_so_far.append(output)
+        self._write_outputs(self.outputs_so_far, run_output)
+        if not self.artifact_added:
+            run_output.add_artifact('run_result_csv', 'results.csv', 'export')
+            self.artifact_added = True
+
+    def process_run_output(self, output, target_info):
+        self.outputs_so_far.append(output)
+        self._write_outputs(self.outputs_so_far, output)
+        if not self.artifact_added:
+            output.add_artifact('run_result_csv', 'results.csv', 'export')
+            self.artifact_added = True
+
+    def _write_outputs(self, outputs, output):
+        if self.use_all_classifiers:
+            classifiers = set([])
+            for out in outputs:
+                for metric in out.metrics:
+                    classifiers.update(metric.classifiers.keys())
+            extra_columns = list(classifiers)
+        elif self.extra_columns:
+            extra_columns = self.extra_columns
+        else:
+            extra_columns = []
+
+        outfile = output.get_path('results.csv')
+        with open(outfile, 'wb') as wfh:
+            writer = csv.writer(wfh)
+            writer.writerow(['id', 'workload', 'iteration', 'metric', ] +
+                            extra_columns + ['value', 'units'])
+
+            for o in outputs:
+                if o.kind == 'job':
+                    header = [o.id, o.label, o.iteration]
+                elif o.kind == 'run':
+                    # Should be a RunOutput. Run-level metrics aren't attached
+                    # to any job so we leave 'id' and 'iteration' blank, and use
+                    # the run name for the 'label' field.
+                    header = [None, o.info.run_name, None]
+                else:
+                    raise RuntimeError(
+                        'Output of kind "{}" unrecognised by csvproc'.format(o.kind))
+
+                for metric in o.result.metrics:
+                    row = (header + [metric.name] +
+                           [str(metric.classifiers.get(c, ''))
+                            for c in extra_columns] +
+                           [str(metric.value), metric.units or ''])
+                    writer.writerow(row)

wa/output_processors/status.py

@@ -0,0 +1,58 @@
+#    Copyright 2013-2015 ARM Limited
+#
+# 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.
+#
+
+
+# pylint: disable=R0201
+import time
+from collections import Counter
+
+from wa import OutputProcessor, Status
+from wa.utils.misc import write_table
+
+
+class StatusTxtReporter(OutputProcessor):
+    name = 'status'
+    description = """
+    Outputs a txt file containing general status information about which runs
+    failed and which were successful
+
+    """
+
+    def process_run_output(self, output, target_info):
+        counter = Counter()
+        for jo in output.jobs:
+            counter[jo.status] += 1
+
+        outfile = output.get_path('status.txt')
+        self.logger.info('Status available in {}'.format(outfile))
+        with open(outfile, 'w') as wfh:
+            wfh.write('Run name: {}\n'.format(output.info.run_name))
+            wfh.write('Run status: {}\n'.format(output.status))
+            wfh.write('Date: {}\n'.format(time.strftime("%c")))
+            if output.events:
+                wfh.write('Events:\n')
+                for event in output.events:
+                    wfh.write('\t{}\n'.format(event.summary))
+
+            txt = '{}/{} iterations completed without error\n'
+            wfh.write(txt.format(counter[Status.OK], len(output.jobs)))
+            wfh.write('\n')
+            status_lines = [map(str, [o.id, o.label, o.iteration, o.status,
+                                      o.event_summary])
+                            for o in output.jobs]
+            write_table(status_lines, wfh, align='<<>><')
+
+        output.add_artifact('run_status_summary', 'status.txt', 'export')
+

wa/output_processors/targz.py

@@ -0,0 +1,63 @@
+import shutil
+import tarfile
+
+from wa import OutputProcessor, Parameter
+from wa.framework import signal
+
+
+class TargzProcessor(OutputProcessor):
+
+    name = 'targz'
+
+    description = '''
+    Create a tarball of the output directory.
+
+    This will create a gzip-compressed tarball of the output directory. By
+    default, it will be created at the same level and will have the same name
+    as the output directory but with a .tar.gz extensions.
+    '''
+
+    parameters = [
+        Parameter('outfile',
+                  description='''
+                  The name  of the output file to be used. If this is not an
+                  absolute path, the file will be created realtive to the
+                  directory in which WA was invoked. If this contains
+                  subdirectories, they must already exist.
+
+                  The name may contain named format specifiers. Any of the
+                  ``RunInfo`` fields can be named, resulting in the value of
+                  that filed (e.g. ``'start_time'``) being formatted into the
+                  tarball name.
+
+                  By default, the output file will be created at the same
+                  level, share the name of the WA output directory (but with
+                  .tar.gz extension).
+                  '''),
+        Parameter('delete-output', kind=bool, default=False,
+                  description='''
+                  if set to ``True``, WA output directory will be deleted after
+                  the tarball is created.
+                  '''),
+    ]
+
+    def initialize(self):
+        if self.delete_output:
+            self.logger.debug('Registering RUN_FINALIZED handler.')
+            signal.connect(self.delete_output_directory, signal.RUN_FINALIZED, priority=-100)
+
+    def export_run_output(self, run_output, target_info):
+        if self.outfile:
+            outfile_path = self.outfile.format(**run_output.info.to_pod())
+        else:
+            outfile_path = run_output.basepath.rstrip('/') + '.tar.gz'
+
+        self.logger.debug('Creating {}'.format(outfile_path))
+        with tarfile.open(outfile_path, 'w:gz') as tar:
+            tar.add(run_output.basepath)
+
+    def delete_output_directory(self, context):
+        self.logger.debug('Deleting output directory')
+        shutil.rmtree(context.run_output.basepath)
+
+

wa/output_processors/uxperf.py

@@ -0,0 +1,47 @@
+
+from wa import OutputProcessor
+from wa.utils.android import LogcatParser
+
+
+class UxperfProcessor(OutputProcessor):
+
+    name = 'uxperf'
+
+    def process_job_output(self, output, target_info, job_output):
+        logcat = output.get_artifact('logcat')
+        if not logcat:
+            return
+
+        parser = LogcatParser()
+        start_times = {}
+
+        filepath = output.get_path(logcat.path)
+        for entry in parser.parse(filepath):
+            if not entry.tag == 'UX_PERF':
+                continue
+
+            parts = entry.message.split()
+            if len(parts) != 3:
+                message = 'Unexpected UX_PERF message @ {}: {}'
+                self.logger.warning(message.format(entry.timestamp, entry.message))
+                continue
+
+            action, state, when = parts
+            when = int(when)
+            if state == 'start':
+                if action in start_times:
+                    self.logger.warning('start before end @ {}'.format(entry.timestamp))
+                start_times[action] = when
+            elif state == 'end':
+                start_time = start_times.pop(action, None)
+                if start_time is None:
+                    self.logger.warning('end without start @ {}'.format(entry.timestamp))
+                    continue
+
+                duration = (when - start_time) / 1000
+                metric_name = '{}_duration'.format(action)
+                output.add_metric(metric_name, duration, 'microseconds',
+                                  lower_is_better=True)
+
+            else:
+                self.logger.warning('Unexpected state "{}" @ {}'.format(state, entry.timestamp))