def _generate_report_for_date()

in report/generate_report.py [0:0]

• 109 lines of code
• 5 McCabe index (conditional complexity)

def _generate_report_for_date(project, date, storage_bucket, report_name,
                              upload_report, bq_project, bq_table):
  """Generates a html report for the specified date & project.

  Args:
    project: the project to generate report for. Check out bazel_bench.py.
    date: the date to generate report for.
    storage_bucket: the Storage bucket to fetch data from/upload the report to.
    report_name: the name of the report on GS.
    upload_report: whether to upload the report to GCS.
    bq_project: the BigQuery project.
    bq_table: the BigQuery table.
  """
  dated_subdir = _get_dated_subdir_for_project(project, date)
  bq_date_cutoff = (date + datetime.timedelta(days=1)).strftime("%Y-%m-%d")
  root_storage_url = _get_storage_url(storage_bucket, dated_subdir)
  metadata_file_url = "{}/METADATA".format(root_storage_url)
  metadata = _load_json_from_remote_file(metadata_file_url)

  graph_components = []
  raw_files_components = []
  graph_components.append(_uncollapse_button("commits", "Show commits"))
  graph_components.append(
      _row_component(
          _col_component(
              "col-sm-10",
              _commits_component(metadata["all_commits"],
                                 metadata["benchmarked_commits"]))))

  for platform_measurement in sorted(
      metadata["platforms"], key=lambda k: k["platform"]):
    # Get the data
    performance_data = _load_csv_from_remote_file("{}/{}".format(
        root_storage_url, platform_measurement["perf_data"]))
    aggr_json_profile = _load_csv_from_remote_file("{}/{}".format(
        root_storage_url, platform_measurement["aggr_json_profiles"]))
    summary_text = _load_txt_from_remote_file("{}/{}".format(
        root_storage_url,
        platform_measurement["perf_data"].replace(".csv", ".txt")))

    wall_data, memory_data = _prepare_data_for_graph(performance_data,
                                                     aggr_json_profile)
    platform = platform_measurement["platform"]

    historical_wall_data, historical_mem_data = _prepare_time_series_data(
        _query_bq(bq_project, bq_table, metadata["project_source"],
                  bq_date_cutoff, platform))

    # Generate a graph for that platform.
    row_content = []
    row_content.append(
        _col_component(
            "col-sm-6",
            _single_graph(
                metric="wall",
                metric_label="Wall Time (s)",
                data=wall_data,
                platform=platform,
                median_series=len(EVENTS_ORDER))))

    row_content.append(
        _col_component(
            "col-sm-6",
            _historical_graph(
                metric="wall",
                metric_label="Wall Time (s)",
                data=historical_wall_data,
                platform=platform,
                color="#dd4477")))

    row_content.append(
        _col_component(
            "col-sm-6",
            _single_graph(
                metric="memory",
                metric_label="Memory (MB)",
                data=memory_data,
                platform=platform,
            )))

    row_content.append(
        _col_component(
            "col-sm-6",
            _historical_graph(
                metric="memory",
                metric_label="Memory (MB)",
                data=historical_mem_data,
                platform=platform,
                color="#3366cc")))

    row_content.append(
        _col_component(
            "col-sm-12",
            _uncollapse_button("summary-{}".format(platform),
                               "Show Summary Table")))
    row_content.append(
        _col_component("col-sm-12",
                       _summary_table(content=summary_text, platform=platform)))

    graph_components.append(
        _row_component(
            _col_component(
                "col-sm-5",
                '<h2 class="underlined">{}</h2></hr>'.format(platform))))
    raw_files_components.append(
        _uncollapse_button("raw_files_%s" % platform,
                           "Show raw files for %s" % platform))
    raw_files_components.append(
        _row_component(
            _col_component(
                "col-sm-10",
                _get_file_list_component(storage_bucket, dated_subdir,
                                         platform))))
    graph_components.append(_row_component("\n".join(row_content)))

  content = _full_report(
      project,
      metadata["project_source"],
      date,
      command=metadata["command"],
      graph_components="\n".join(graph_components),
      raw_files_components="\n".join(raw_files_components))

  if not os.path.exists(REPORTS_DIRECTORY):
    os.makedirs(REPORTS_DIRECTORY)

  report_tmp_file = "{}/report_{}_{}.html".format(REPORTS_DIRECTORY, project,
                                                  date.strftime("%Y%m%d"))
  with open(report_tmp_file, "w") as fo:
    fo.write(content)

  if upload_report:
    _upload_to_storage(report_tmp_file, storage_bucket,
                       dated_subdir + "/{}.html".format(report_name))
  else:
    print(content)