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_vectors_start_end_time_at_load(): trimmed = results.read_result_files(RESULT_FILES, vector_start_time=40, vector_end_time=60) df = results.get_vectors(trimmed) _assert_sequential_index(df) _assert(sanitize_and_compare_csv(df, "vectors_start_end_time.csv"), "content mismatch")
def test_runs_without_config_entries(): df = results.get_runs(r, include_itervars=True, include_runattrs=True, include_param_assignments=True, include_config_entries=False) _assert_sequential_index(df) _assert(sanitize_and_compare_csv(df, "runs_without_config_entries.csv"), "content mismatch")
def test_statistics_with_all(): df = results.get_statistics(r, include_attrs=True, include_itervars=True, include_runattrs=True, include_param_assignments=True, include_config_entries=True) _assert_sequential_index(df) _assert(sanitize_and_compare_csv(df, "statistics_with_all.csv"), "content mismatch")
def test_param_assignments_with_all(): df = results.get_param_assignments(r, include_itervars=True, include_runattrs=True, include_param_assignments=True, include_config_entries=True) _assert_sequential_index(df) _assert(sanitize_and_compare_csv(df, "param_assignments_with_all.csv"), "content mismatch") _assert( df.apply(lambda r: r[r["name"]] == r["value"], axis=1).all(), "wrong join")
def test_parameters_with_attrs(): df = results.get_parameters(r, include_attrs=True) _assert_sequential_index(df) # these parameters don't have any attrs _assert(sanitize_and_compare_csv(df, "parameters.csv"), "content mismatch")
def test_parameters(): df = results.get_parameters(r) _assert_sequential_index(df) _assert(sanitize_and_compare_csv(df, "parameters.csv"), "content mismatch")
def test_statistics_with_attrs(): df = results.get_statistics(r, include_attrs=True) _assert_sequential_index(df) _assert(sanitize_and_compare_csv(df, "statistics_with_attrs.csv"), "content mismatch")
def test_runs_with_config_entries(): df = results.get_runs(r, include_config_entries=True) _assert_sequential_index(df) _assert(sanitize_and_compare_csv(df, "runs_with_config_entries.csv"), "content mismatch")
def test_vectors_end_time(): trimmed = results.read_result_files(RESULT_FILES) df = results.get_vectors(trimmed, end_time=80) _assert_sequential_index(df) _assert(sanitize_and_compare_csv(df, "vectors_end_time.csv"), "content mismatch")
def test_scalars_with_param_assignments(): df = results.get_scalars(r, include_param_assignments=True) _assert_sequential_index(df) _assert(sanitize_and_compare_csv(df, "scalars_with_param_assignments.csv"), "content mismatch")
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_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_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)