def test_unused_columns(): df = results.get_results(r, row_types=["itervar"], omit_unused_columns=False) _assert_sequential_index(df) # two replications of three measurements of a single experiment, and 19 columns in total return df.shape == (6, 19)
def test_row_type_filter_2(): filtered = results.read_result_files(RESULT_FILES, "run =~ *General-0* AND module =~ Test.node1 AND name =~ foo1*") df = results.get_results(filtered, row_types=["scalar", "attr"]) _assert_sequential_index(df) # 2 times 3 rows for scalars (incl. value), and 3 times 2 rows for the vector, stats, and histogram (only attr) # since we only filtered for row types, not result types, we get the attrs for the other kinds of results too, just not the results themselves return df.shape == (12, 7)
def test_vector_time_limit_at_load_2(): filtered = results.read_result_files( RESULT_FILES, "type =~ vector AND run =~ General-0*", vector_end_time=50.0) df = results.get_results(filtered, row_types=["vector"]) _assert_sequential_index(df) return df["vectime"].map(lambda a: (a < 50.0).all()).all()
def test_vector_data(): filtered = results.read_result_files( RESULT_FILES, "type =~ vector AND run =~ General-0*") df = results.get_results(filtered, row_types=["vector"]) _assert_sequential_index(df) return df["vectime"].map(lambda a: a.shape == (100, )).all()
def test_row_type_filter_3(): filtered = results.read_result_files(RESULT_FILES, "type =~ param") df = results.get_results(filtered, row_types=["attr"]) _assert_sequential_index(df) # params don't have attrs return df.empty
def test_row_type_filter_1(): df = results.get_results(r, row_types=["scalar"]) _assert_sequential_index(df) # two recorded values from two sources of two submodules in all six runs return df.shape == (48, 5)
def test_result_filter(): filtered = results.read_result_files(RESULT_FILES, "type =~ scalar") df = results.get_results(filtered) _assert_sequential_index(df) # in all 6 runs: 20 lines of metadata, and 4 lines (1 scalar and 3 attrs) for all 8 scalars return df.shape == (312, 7)
def test_config_count(): df = results.get_results(r, row_types=["config"]) _assert_sequential_index(df) return df["type"].map(lambda t: t == "config").all() and df.shape == (18, 4)
def test_itervar_count(): df = results.get_results(r, row_types=["itervar"]) _assert_sequential_index(df) return df["type"].map(lambda t: t == "itervar").all() and df.shape == (6, 4)
def test_results(): df = results.get_results(r) _assert_sequential_index(df) _assert(sanitize_and_compare_csv(df, "results.csv"), "content mismatch")